Welcome to the Knowledge Agenda on Automatic Driving, an initiative of the Ministry of Infrastructure and Water Management, Department of Transport and the RDW-Vehicle-approval, to provide an online overview of available and required knowledge in the field of automatic driving.

The overview is divided into a number of knowledge domains to map the various facets. In the library you will find an extensive collection of reports, papers and presentations, including summaries and background information. The library is used worldwide. The last report on Ethics was requested 674 times in a short time! About 30 pieces are downloaded every day.

The collection of knowledge documents is managed in Dropbox. With Dropbox you can search directly in the folders with documents and full text. Contact joop@veenis.net to gain access to the Dropbox.

Since 2015 we keep a list of knowledge questions (the required knowledge). Our collection of documents provides an answer to these knowledge questions. New questions are coming up because we are getting further and further into the implementation of “Connected Automated Driving”. The set of knowledge questions includes the topics automated driving and Smart Mobility (ITS). Additional overviews with projects are available here on the ITS theme. Experts on themes also develop knowledge and standards in the Netherlands/EU; an overview can be found here.

The popular knowledge questions are:

DEPLOYMENT-Businessmodels

There are multiple models/players:nnThe Branded Integrated Life-Style ModelnnIt’s a sleekly designed experience, riding in this self-driving car. As elegantly designed as the sleekest smart phone.You use an app on your phone to summon your car when you need it or to program a daily pick-up. It’s as simple as setting the alarm on your phone.Your windshield doubles as a screen, synching seamlessly with your other connected devices. As you ride along, you swipe through applications and web sites, checking your progress and the local weather on a digital dashboard, uploading photos to your favorite web site or watching a video. When you arrive at your destination, the screens you’ve opened are synched and waiting for you on whatever device you pick up next.nnIn this model, perhaps a company with no traditional presence in the auto industry that is already an integral part of the consumer’s life outside the vehicle could become a key participant in the ecosystem. Since self-driving vehicles will no longer need the same level of rigorous testing and validation, and manufacturing could potentially be outsourced, their emphasis would be on consumer research, product development, and sale of integrated lifestyle experiences.nnThe Branded Lifestyle Value Proposition: Design, Technology, Software, Consumer experience nnThe Open System ModelnnIt’s all about the data and how to use these data to customize the consumer value proposition.The market for big datannis growing exponentially. Market intelligence provider IDC predicts that by 2015 the “Big Data” market will be $16.9 billion, up from $3.2 billion in 2010.35 A major player in the data market might not want to manufacture vehicles, but couldnnwell design a vehicle operating system. With more than a billion cars serving up trillions of data points about consumer behavior, traffic patterns, and topography, an operating system (OS) developer could afford to give away the OS but accrue significant value from the data they could aggregate. Who would manufacture the vehicle? The OS provider could partner with any of the world’s vehicle manufacturers—and not just the traditional automotive manufacturers. Partnerships could be established with one or more new players who might compete in the branded technology arena.nnThe Open System Value Proposition: Utility, Technology, Customization nn Mobility On Demand ModelnnZipcar was the pioneer in the shared-vehicle field, but other players are breaking into the market. Whereas current mobility on demand providers must make vehicles easily accessible for customers in urban areas, their vehicle maintenance and parking fees are high. With self-driving vehicles, proximity to end-users would no longer be necessary. Vehicles could be dispatched by taxi and car service companies.nnGiant retailers with a core competence in managing complex distribution channels or fleet providers with the capabilitynnto manage the complexity of renting and allocation of fleets could enter the fray and accrue significant value in the new ecosystem. New entrants in the market might compete at either end of the spectrum—with generic, low-cost utilitarian transportation on demand at one end (the low-cost airline model) and super-luxury mobile executive suites and sleeping pods at the other (the first class or private jet experience). Success will be determined by efficiency, reliability, flexibility, vehicle maintenance, customer service, ease of human-vehicle interface, and integration with existing consumer devices—and all the other psychographic factors that determine consumer behaviors and brand preferences.nnThe Mobility on Demand Value Proposition: Flexibility, Reliability, Convenience, Cost nnThe OEM ModelnnTraditional automotive manufacturers have decades of experience in designing and manufacturing vehicles, and shaping an emotional connection with consumers. But will they move fast enough to maintain their brand dominance? Smart automotive manufacturers should be planning now, thinking about how to restructure their organizations and what potential strategic investments they should be making. History has not been kind to those who get stuck protecting the status quo in the face of disruptive change. In fact, collaboration is already taking place across the ecosystem as companies strive to stay relevant.The joint project between Intel and DENSO36 to develop in-vehicle communication and information systems exemplifies the new cross-industry synergistic relationships.nnVertical integration is an option for companies looking to bring a critical skill or technology in house. Some vehicle manufacturers have established venture capital subsidiaries to invest in promising new technologies as a means of bridging any skill or technology gaps. Doing so may provide a competitive advantage in this rapidly evolving ecosystem.nnThe OEM Value Proposition: Design, Technology, HMI, Supply Chain Management “nnFound on (p.32-33): Self-Driving Cars, The Next Revolution

Regarding making PT more flexible:nnSuch a transformation of the system could also breathe new life into ideas of financing basic public-transportation services—on the one hand in the form of pay-as-you-drive, but also on a flat-rate basis financed via taxes or levied on all citizens, as is often debated for cities. Also, a high service density in suburban and even rural areas would justify a flat-rate levy and could in the process help to reduce private car use.”nnRegarding offering new service options for PT:nnConcerning intermodality, possibilities include more public transport services, even in the suburban and rural areas mentioned above (for urban areas, see Chap. 11). The benefits resulting from the use of autonomous vehicles are equally true in spatial and temporal terms, that is both for districts on the outskirts and off-peak hours. An economic lower limit resulting from frequency of use also applies here, however, even in view of the saved labor costs. This also means that a spatially highly dispersed use can only be covered to a limited extent by providing larger fleets. In any case, operating these vehicles would have to pay for itself in terms of initial outlay and operating costs.”nnFound on (p. 186 & 187): New Mobility Concepts and Autonomous Driving: The Potential for Change

“Transport data analysis revealed that most feasible platooning trips have their origin and destination relatively close to each other (less than 100km).  nWhen fuel consumption saving is only benefit:. The shorter the distance the more bennefit, see tabel 4.2 in (p. 34 & 36): Truck Platooning – Enablers, Barriers, Potential and Impactnn 

Another deployment scenario for automated driving involves implementing transportation paradigms that provide slow-moving passenger vehicles, for example in urban areas. Consumers could summon such vehicles using a smartphone app and ride them over relatively short distances (see the use case “vehicle on demand”). In particular, companies from unrelated sectors can use image processing, object recognition, and route planning systems—which are already in widespread modular use—to implement transportation models with higher-order automation within a limited geographical range. The arrangements often proposed for market introduction are slow-moving and limited-area vehicles intended to serve what is known as the “first or last mile,” complementary to private automobiles or public transportation. To name one concrete example, these types of solutions could be used to reach bus and urban rail networks in areas where a regular schedule is not feasible due to inadequate infrastructure or financial limitations. Found on (p.199-200): https://www.dropbox.com/s/91n2z7i19wfgzu6/Beiker%2C%20S.%20%282016%29%20Deployment%20Scenarios%20for%20Vehicles%20with%20Higher-Order%20Automation.pdf?dl=0nn Note Joop: In the Netherlands there are tests concerning a connection with the station Ede and the Campus of Wageningen (WEPODS). In 2018 there will be tests with the WEPOD to the airport Weeze in Germany. There are also some test in the region MRDH near Airport Rotterdam.

The automotive industry is a global industry in which value is generated predominantly by suppliers to automakers. The Dutch automotive industry is no exception to this rule. Within specific areas in the automotive industry, the Netherlands even plays a significant role with leading innovative companies that are involved in automotive activities worldwide. In these areas, the Dutch automotive sector is highly innovative and possesses a considerable knowledge base. To further strengthen its role, the Dutch automotive sector has developed a vision supported by a strong ambition of the Dutch automotive industry to increase its annual revenues from Eur 12 bn to Eur 20 bn.nnThe Dutch automotive sector has two responses to the opportunities and challenges of today’s automotive industry: innovation and cooperation. Innovation is vital in the continuous struggle for cost reductions alongside increasing levels of quality, individuality, and personalisation, and legal requirements (e.g., noise, safety and emission). Effective cooperation is becoming more and more crucial as competitive advantage will gravitate towards those that discern their strengths and move quickly to build or join appropriate new collaborative networks.” nn nnFound on (p.7): Vision for the Dutch automotive sector

DEPLOYMENT-Cooperation-alliances-partnerships

“According to (Anderson, et al. 2014) Self-propelled vehicles could considerably upsurge access and movement across a variety of populations presently incapable or not permitted to use conventional automobile. These include the incapacitated, older people, and children of age 16 or less. The most promising advantages would be personal independence, increased sociability, and access to vital services. Level 4 automation is expected to provide mobility and access at reduced cost when compared to the current system which provides mobility services for disabled for 14 to 18 percent of their budgets in the U.S. “nnFound on (p.42): Impact of Autonomous Vehicles on Urban Mobilitynn“This type of combined and improved transport systems come with many advantages like they would reduce the necessity for roads and parking; lessen congestion, air pollution and greenhouse gas emissions; would support the optimization of capitals used for transportation; and upsurge the living standards in the region. “nnFound on (p.39): Impact of Autonomous Vehicles on Urban Mobility nn“Considering that off-street parking represents 50 000 spots in the baseline case and that the most parking- intensive scenario (car sharing without public transport) would require 25 621 spots, on-street parking spots could be totally removed from the streets, whatever the scenario considered. This would allow the reallocation of 1 530 000 m2 to other public uses2, equivalent to almost 20% of the kerb-to-kerb street area in Lisbon or 210 football fields. This freed-up space could be dedicated to non-motorised transport modes, delivery bays, parklets or other recreational and commercial uses. “nnFound on (p.26): Urban Mobility Upgrade

Indirect answer:nn“There have been numerous predicted monetary advantages linked to the adaptation of Autonomous Vehicles for daily commuting and travelling. First estimations state that self-driving cars can contribute up to $1.3 trillion in annual savings to the United States economy alone, and an expected sum of $5.6 trillion for global savings (Bartl 2015). “nnFound on (p.30): Impact of Autonomous Vehicles on Urban Mobility nn

The opinions are divided, depending on different assumptions about the future: nnProbable nothing imported in explained in the following article. :nn“Less likely, even if repeatedly cited by the scientific community, is the conversion of the entire vehicle fleet into vehicles that are on the road on a sharing basis or operated by public transport providers. There are currently no indications that private cars are losing any of their attraction.” nnScenario with individual Public TransportnnOne possible first step in individualizing public transport could be company buses, such as the so-called Google Bus, equipped with WiFi access and operating in and around San Francisco, which brings the company’s employees to work. In this case, a specific community gets together in a communal shuttle. Comparable concepts, albeit in manifold varieties, are conceivable and appear particularly attractive when based on autonomously driven vehicles. “nnFound on (p. 180 & 186):nnhttps://www.dropbox.com/s/i3wankjtjm4y1xc/Lenz%2C%20B.%20%26%20Fraedrich%2C%20E.%20%282016%29%20New%20Mobility%20Concepts%20and%20Autonomous%20Driving%20-%20The%20Potential%20for%20Change.pdf?dl=0nn

Voor AMOD (automated on demand):nn“Despite the largely independent operating characteristics of an AMOD system, there are several special conditions that warrant consideration. Firstly, operating personnel are required to safeguard operation. While it is not necessary for staff to oversee every individual steering, braking or drive command, but they must ensure safety of general operation within the established operating area (with its geographical limits) and specified parameters (e.g. speed or position). To achieve this, operation centers are established similar to those used for driverless trains or for logistics systems. Those centers are staffed with comparatively small numbers of personnel who monitor a large number of vehicles. The operating center is connected via radio to the individual vehicles and is capable of monitoring vehicle operating data, halting the vehicle in an emergency and communicating with passengers.” nn nnFound on (p.281):nnhttps://www.dropbox.com/s/3cr7xmonxcp6np2/Beiker%2C%20S.%20%282016%29%20Implementation%20of%20an%20Automated%20Mobility-on-Demand%20System.pdf?dl=0

DEPLOYMENT-Future-explorations-and-transition-path

There are multiple models/players:nnThe Branded Integrated Life-Style ModelnnIt’s a sleekly designed experience, riding in this self-driving car. As elegantly designed as the sleekest smart phone.You use an app on your phone to summon your car when you need it or to program a daily pick-up. It’s as simple as setting the alarm on your phone.Your windshield doubles as a screen, synching seamlessly with your other connected devices. As you ride along, you swipe through applications and web sites, checking your progress and the local weather on a digital dashboard, uploading photos to your favorite web site or watching a video. When you arrive at your destination, the screens you’ve opened are synched and waiting for you on whatever device you pick up next.nnIn this model, perhaps a company with no traditional presence in the auto industry that is already an integral part of the consumer’s life outside the vehicle could become a key participant in the ecosystem. Since self-driving vehicles will no longer need the same level of rigorous testing and validation, and manufacturing could potentially be outsourced, their emphasis would be on consumer research, product development, and sale of integrated lifestyle experiences.nnThe Branded Lifestyle Value Proposition: Design, Technology, Software, Consumer experience nnThe Open System ModelnnIt’s all about the data and how to use these data to customize the consumer value proposition.The market for big datannis growing exponentially. Market intelligence provider IDC predicts that by 2015 the “Big Data” market will be $16.9 billion, up from $3.2 billion in 2010.35 A major player in the data market might not want to manufacture vehicles, but couldnnwell design a vehicle operating system. With more than a billion cars serving up trillions of data points about consumer behavior, traffic patterns, and topography, an operating system (OS) developer could afford to give away the OS but accrue significant value from the data they could aggregate. Who would manufacture the vehicle? The OS provider could partner with any of the world’s vehicle manufacturers—and not just the traditional automotive manufacturers. Partnerships could be established with one or more new players who might compete in the branded technology arena.nnThe Open System Value Proposition: Utility, Technology, Customization nn Mobility On Demand ModelnnZipcar was the pioneer in the shared-vehicle field, but other players are breaking into the market. Whereas current mobility on demand providers must make vehicles easily accessible for customers in urban areas, their vehicle maintenance and parking fees are high. With self-driving vehicles, proximity to end-users would no longer be necessary. Vehicles could be dispatched by taxi and car service companies.nnGiant retailers with a core competence in managing complex distribution channels or fleet providers with the capabilitynnto manage the complexity of renting and allocation of fleets could enter the fray and accrue significant value in the new ecosystem. New entrants in the market might compete at either end of the spectrum—with generic, low-cost utilitarian transportation on demand at one end (the low-cost airline model) and super-luxury mobile executive suites and sleeping pods at the other (the first class or private jet experience). Success will be determined by efficiency, reliability, flexibility, vehicle maintenance, customer service, ease of human-vehicle interface, and integration with existing consumer devices—and all the other psychographic factors that determine consumer behaviors and brand preferences.nnThe Mobility on Demand Value Proposition: Flexibility, Reliability, Convenience, Cost nnThe OEM ModelnnTraditional automotive manufacturers have decades of experience in designing and manufacturing vehicles, and shaping an emotional connection with consumers. But will they move fast enough to maintain their brand dominance? Smart automotive manufacturers should be planning now, thinking about how to restructure their organizations and what potential strategic investments they should be making. History has not been kind to those who get stuck protecting the status quo in the face of disruptive change. In fact, collaboration is already taking place across the ecosystem as companies strive to stay relevant.The joint project between Intel and DENSO36 to develop in-vehicle communication and information systems exemplifies the new cross-industry synergistic relationships.nnVertical integration is an option for companies looking to bring a critical skill or technology in house. Some vehicle manufacturers have established venture capital subsidiaries to invest in promising new technologies as a means of bridging any skill or technology gaps. Doing so may provide a competitive advantage in this rapidly evolving ecosystem.nnThe OEM Value Proposition: Design, Technology, HMI, Supply Chain Management “nnFound on (p.32-33): Self-Driving Cars, The Next Revolution

“One central aspect of human-machine interaction is the perceived autonomy of the consumer [4, 29]. While the role of consumer autonomy has been addressed directly or indirectly by some studies, its criticality for consumer acceptance of automated technologies might not be fully captured in the contexts studied. Restricting or removing the autonomy of individuals could cause reactance, i.e., negative psychological and contrary behavioral responses of consumers as reactions to a perceived restriction of their personal freedoms [6, 44]. Automated driving systems could be perceived as a threat to drivers’ autonomy, and reactance could arise in terms of consumer boycott intentions or low adoption rates. Presently, it it is unclear if consumers are willing to accept a loss in control [56].”nnFound on (p.690): Consumer Perceptions of Automated Driving Technologies: An Examination of Use Cases and Branding Strategiesnn

Another deployment scenario for automated driving involves implementing transportation paradigms that provide slow-moving passenger vehicles, for example in urban areas. Consumers could summon such vehicles using a smartphone app and ride them over relatively short distances (see the use case “vehicle on demand”). In particular, companies from unrelated sectors can use image processing, object recognition, and route planning systems—which are already in widespread modular use—to implement transportation models with higher-order automation within a limited geographical range. The arrangements often proposed for market introduction are slow-moving and limited-area vehicles intended to serve what is known as the “first or last mile,” complementary to private automobiles or public transportation. To name one concrete example, these types of solutions could be used to reach bus and urban rail networks in areas where a regular schedule is not feasible due to inadequate infrastructure or financial limitations. Found on (p.199-200): https://www.dropbox.com/s/91n2z7i19wfgzu6/Beiker%2C%20S.%20%282016%29%20Deployment%20Scenarios%20for%20Vehicles%20with%20Higher-Order%20Automation.pdf?dl=0nn Note Joop: In the Netherlands there are tests concerning a connection with the station Ede and the Campus of Wageningen (WEPODS). In 2018 there will be tests with the WEPOD to the airport Weeze in Germany. There are also some test in the region MRDH near Airport Rotterdam.

“Automated driving, with its minimal space requirements and rather equal speed levels, could at least double the existing average road infrastructure capacity. “nnFound on (p.380): Autonomous Vehicles and Autonomous Driving in Freight Transportnn 

“According to (Anderson, et al. 2014) Self-propelled vehicles could considerably upsurge access and movement across a variety of populations presently incapable or not permitted to use conventional automobile. These include the incapacitated, older people, and children of age 16 or less. The most promising advantages would be personal independence, increased sociability, and access to vital services. Level 4 automation is expected to provide mobility and access at reduced cost when compared to the current system which provides mobility services for disabled for 14 to 18 percent of their budgets in the U.S. “nnFound on (p.42): Impact of Autonomous Vehicles on Urban Mobilitynn“This type of combined and improved transport systems come with many advantages like they would reduce the necessity for roads and parking; lessen congestion, air pollution and greenhouse gas emissions; would support the optimization of capitals used for transportation; and upsurge the living standards in the region. “nnFound on (p.39): Impact of Autonomous Vehicles on Urban Mobility nn“Considering that off-street parking represents 50 000 spots in the baseline case and that the most parking- intensive scenario (car sharing without public transport) would require 25 621 spots, on-street parking spots could be totally removed from the streets, whatever the scenario considered. This would allow the reallocation of 1 530 000 m2 to other public uses2, equivalent to almost 20% of the kerb-to-kerb street area in Lisbon or 210 football fields. This freed-up space could be dedicated to non-motorised transport modes, delivery bays, parklets or other recreational and commercial uses. “nnFound on (p.26): Urban Mobility Upgrade

HUMAN-FACTORS-Acceptance

“One central aspect of human-machine interaction is the perceived autonomy of the consumer [4, 29]. While the role of consumer autonomy has been addressed directly or indirectly by some studies, its criticality for consumer acceptance of automated technologies might not be fully captured in the contexts studied. Restricting or removing the autonomy of individuals could cause reactance, i.e., negative psychological and contrary behavioral responses of consumers as reactions to a perceived restriction of their personal freedoms [6, 44]. Automated driving systems could be perceived as a threat to drivers’ autonomy, and reactance could arise in terms of consumer boycott intentions or low adoption rates. Presently, it it is unclear if consumers are willing to accept a loss in control [56].”nnFound on (p.690): Consumer Perceptions of Automated Driving Technologies: An Examination of Use Cases and Branding Strategiesnn

“According to (Anderson, et al. 2014) Self-propelled vehicles could considerably upsurge access and movement across a variety of populations presently incapable or not permitted to use conventional automobile. These include the incapacitated, older people, and children of age 16 or less. The most promising advantages would be personal independence, increased sociability, and access to vital services. Level 4 automation is expected to provide mobility and access at reduced cost when compared to the current system which provides mobility services for disabled for 14 to 18 percent of their budgets in the U.S. “nnFound on (p.42): Impact of Autonomous Vehicles on Urban Mobilitynn“This type of combined and improved transport systems come with many advantages like they would reduce the necessity for roads and parking; lessen congestion, air pollution and greenhouse gas emissions; would support the optimization of capitals used for transportation; and upsurge the living standards in the region. “nnFound on (p.39): Impact of Autonomous Vehicles on Urban Mobility nn“Considering that off-street parking represents 50 000 spots in the baseline case and that the most parking- intensive scenario (car sharing without public transport) would require 25 621 spots, on-street parking spots could be totally removed from the streets, whatever the scenario considered. This would allow the reallocation of 1 530 000 m2 to other public uses2, equivalent to almost 20% of the kerb-to-kerb street area in Lisbon or 210 football fields. This freed-up space could be dedicated to non-motorised transport modes, delivery bays, parklets or other recreational and commercial uses. “nnFound on (p.26): Urban Mobility Upgrade

“Governments can anticipate—and possibly even accelerate—this watershed by taking some or all of the actions described in this Article. These strategies, which were identified through extensive discussions with developers and regulators of automated driving systems as well as other emerging technologies, are roughly organized into three overlapping categories: n

    n

  • Administrative strategies include preparing government agencies, preparing public infrastructure, leveraging procurement, and advocating for safety mandates.
  • n

n

    n

  • Legal strategies entail carefully analyzing and, as necessary, clarifying existing law as it applies to automated driving; many of these strategies would also internalize more of the costs of conventional driving in a way that could properly incentivize automated driving.
  • n

  • Community strategies involve identifying specific local needs, opportunities, and resources that may be relevant to automated driving; communities can already start developing proposals in anticipation of public and private grants that may soon be announced. “
  • n

nFound on (p.3):How Governments Can Promote Automated Drivingnn

“100% Public Perceptions of Driverless cars

Page 8: To what extent are repondents afraid for certain events that could happen. Barriers could be made out of these fears. “

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    HUMAN-FACTORS-Behaviour

    30% Intelligent Cruise Control Field Operational Test

     

    As implied above, it is assumed that high levels of ACC penetration into the vehicle population will cause extended strings of ACC-equipped vehicles to form spontaneously simply due to the probabilities of traffic mixing— even in the absence of any peculiar natural tendencies toward aggregation of vehicles under ACC control. Thus, the dynamic stability of ACC strings and their impact on the natural inter lane weaving movements of other traffic will constitute real issues if ACC becomes a successful product. Observations from these tests have indicated that significant traffic impacts could arise from ACC strings. Firstly, considering simply the ACC system that was fielded here, (with its low deceleration authority and relatively sluggish re-acceleration response) a string of more than four of these vehicles will exhibit marginal stability levels, yieldingexaggerated responses when longitudinally disturbed from the forward end of the string. With strings of eight vehicles equipped with this ACC controller, significant disruptions in the smooth movement of a traffic stream would ensue following modest disturbances. Further to the string-stability issue, the authors of this report are not aware that this characteristic is being considered in the current design of automotive ACC products. Infact, an opposite approach has been apparent by which ACC control algorithms are “detuned” in some emerging products to render the controller unresponsive to brief misdetections by the range sensor. While string-stability problems would not manifest themselves as long as ACC-equipped vehicles are a rarity on the road, the issue will become highly important whenever the population density begins to precipitate long string formation on a regular basis. On the matter of cross-lane movements of other traffic, an important issue arises when an ACC string constitutes a sort of “moving wall” that impedes the natural weaving movement of other traffic. That is, due to ACC’s regularization of headway spacing,randomly extended gaps do not occur in the same manner as seen in manually-controlled traffic. Further, the ACC controller does not, by itself, respond to the “body language” of other drivers who maneuver alongside, in an adjacent lane, with the clear intention of weaving across into another destination lane. When headway time is in the vicinity of 1.0second, at highway speed, it was seen that other motorists were basically thwarted in their attempts to change lanes through an eight-car string that occupied the next-to-right-mostlane— occasionally exhibiting a fairly dramatic rate of penetrating the string in their apparent frustration to find a fully suitable gap in line with their exit/entrance transition plans. (Note that, upon entering a freeway, some more aggressive drivers seek to occupy the “fast,” left-most lane as soon as possible— thus experiencing some frustration when they remain “stuck” in the rightmost lane while searching for a suitable gap.) When ACC headway times were uniformly set to 1.5 seconds, other drivers appeared to penetrate the string with minimal difficulty.

    “One central aspect of human-machine interaction is the perceived autonomy of the consumer [4, 29]. While the role of consumer autonomy has been addressed directly or indirectly by some studies, its criticality for consumer acceptance of automated technologies might not be fully captured in the contexts studied. Restricting or removing the autonomy of individuals could cause reactance, i.e., negative psychological and contrary behavioral responses of consumers as reactions to a perceived restriction of their personal freedoms [6, 44]. Automated driving systems could be perceived as a threat to drivers’ autonomy, and reactance could arise in terms of consumer boycott intentions or low adoption rates. Presently, it it is unclear if consumers are willing to accept a loss in control [56].”nnFound on (p.690): Consumer Perceptions of Automated Driving Technologies: An Examination of Use Cases and Branding Strategiesnn 

    HUMAN-FACTORS-Drivinglessons-and-skills

    0% We are sorry but we do not have much reports and research on this subject.

    Our general view on this matter is that driving will first get more complicated (level 3), since the human driver and the auto-pilot must work together and transition of control is considered full off risks that the human driver should be aware of. From SAE level 4-5 the demands for the human driver will decrease. In the end-scenario (level 5) we picture that the car/autopilot must gain a license, and the human becomes a passenger and forgets how to drive a vehicle. [Joop Veenis]

    A recent report that was just added to our collection can be found here

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    Yes. In the Netherlands this is the situation: Until 1 January 2016 such (level 1) systems should be switched off by the candidate while taking the test to ensure a similar test situation for every candidate, focused on the driving task itself (the use of ADAS changes this driving task). However, to create a realistic and future-proof test situation, it is necessary to implement the use of such systems in driver training and testing. Therefore, CBR allows candidates to use ADAS during the driving test on a voluntary basis. If so, they will be judged on using these systems in relation to a proper performance of the driving task.

    “According to (Anderson, et al. 2014) Self-propelled vehicles could considerably upsurge access and movement across a variety of populations presently incapable or not permitted to use conventional automobile. These include the incapacitated, older people, and children of age 16 or less. The most promising advantages would be personal independence, increased sociability, and access to vital services. Level 4 automation is expected to provide mobility and access at reduced cost when compared to the current system which provides mobility services for disabled for 14 to 18 percent of their budgets in the U.S. “Found on (p.42): Impact of Autonomous Vehicles on Urban Mobility Stress, productivity, road capacity, energy, emissions:“Potentially, autonomous vehicles could significantly reduce stress levels and offer us the capability to rest – or work – while traveling. Driverless cars could also provide independent mobility for non-drivers, increase road capacity and reduce traffic congestion. They could additionally reduce parking costs, accidents, offer energy conservation, emissions reductions as well as more scope for vehicle sharing. “nnFound on (p.037): Ready or WaitingnnKiM recently published a second study around this subject. See library.

    HUMAN-FACTORS-Human-machine-interaction

    “One central aspect of human-machine interaction is the perceived autonomy of the consumer [4, 29]. While the role of consumer autonomy has been addressed directly or indirectly by some studies, its criticality for consumer acceptance of automated technologies might not be fully captured in the contexts studied. Restricting or removing the autonomy of individuals could cause reactance, i.e., negative psychological and contrary behavioral responses of consumers as reactions to a perceived restriction of their personal freedoms [6, 44]. Automated driving systems could be perceived as a threat to drivers’ autonomy, and reactance could arise in terms of consumer boycott intentions or low adoption rates. Presently, it it is unclear if consumers are willing to accept a loss in control [56].”nnFound on (p.690): Consumer Perceptions of Automated Driving Technologies: An Examination of Use Cases and Branding Strategiesnn

    “50% Transition of control in highly automated vehicles A literature review R-2015-22

    According to Flemisch et al. (2012), there are four factors that define the relationship between drivers and highly automated vehicles where the automated systems primarily perform the driving task and the driver performs the driving task occasionally. These factors are: ability, authority, control and responsibility.

    Factors relevant for interaction.

    • Interactie have en have-not’s? Vaste set van indicatoren om rijgedrag te monitoren?

     

    100% Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence.

     

    Many Human Factors researchers would probably agree that workload and situation awareness are two of the most important Human Factors constructs that are predictive of performance and safety (McCauley & Miller, 1997;Parasuraman, Sheridan, & Wickens, 2008;Sarter & Woods, 1991; Stanton & Young, 2000). Accordingly, the aim of this study is to quantify the effects of ACC and

    HAD on workload and situation awareness.

     

    A few indicators to monitor driving behavior, while driving with a selfdriving car.

     

    100% Rijtaakindicatoren voor C-ITS-projecten

     

    Indicators for the purpose of the flow

    Velocity (pointvelocity, average velocity, continue velocity)

    Distance to the car in front

    Lane choice and amount of lane changes

    Acceleration (in particulary by solving congestion)

    Amount of vehicles on the road (situational variables)

    Longitudinal position

    Lateral position

    Use of signaling form cars, for example direction indicator

    Braking behavior

    80% AUTOMATED DRIVING FUNCTIONS GIVING CONTROL BACK TO THE DRIVER

    Did same research but with little participants(16) and in a simulator.

    Objective Results

    The effect of the additional task is evaluated through the reaction time of the drivers on the confirmation request,and the steering behaviour after regaining control and taking the exit. This is shown in Figure 6.

    Figure 6.  graphs with time.

     

    100% Transition of control in highly automated vehicles A literature review R-2015-22

    Measures for vehicle control were the standard deviation of the lateral position (SDLP) and the frequency of steering adjustments. There were two conditions: (1) moments when a switch to manual driving was required while drivers were attentively scanning the forward roadway while the vehicle was in fully automated mode, and (2) at moments the eyetracking equipment indicated that drivers were not attentively scanning the forward roadway while the vehicle was in the fully automated mode. When drivers were attentive, switching to manual and regaining proper control over the vehicle took on average 10 s. When drivers were less attentive when driving in the fully automated mode, switching to manual and regaining full control over the vehicle took circa 35-40 s. These results imply that especially when drivers are not attentive, messages about a switch tomanual must be provided properly and timely. These results also indicate that planned switches to manual driving have to occur in traffic situationswhere crash risk is low.

     

    This is also from a single research, gives precise times. Other results than the article above, but this research is more accurate.

    5/10% AUVSI-TRB-Symposium2015-Presentaties-Human-Factors

     

    S.Hill presentation gives results of the research about time of out-of-loop to in-to-loop.

    1. Green presentation gives a formula to calculate time of taking back control

    K,Lee presentation also gives results of research about time of taking back control.

     

    100% Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence.

    Different experminents, with different times as answer. The answers are divided in big pieces of tekst in the article.

    “100% AUTOMATED DRIVING FUNCTIONS GIVING CONTROL BACK TO THE DRIVER

    At a certain distance upstream from the exit, the participant was warned and requested to provide a confirmation by pushing a button on the touch screen of the interface (Figure 2). If the driver did not confirm within a certain time the warning and confirmation request was repeated. Closer to the exit, irrespective of the driver reacting to the confirmation request, a warning was displayed that provided the amount of meters till the exit (Figure 1 without the confirmation request and button). The timings of the warnings and feedback requests were different between the attentive and inattentive driver states (see Table 1). The unadapted transition strategy was to warn the participant and ask for confirmation the first time at 1000 m before the exit. From 500 m before the exit the participant was continously informed on the distance (‘count down’) till the VTB system would switch off. In the adapted strategy, Willemsen 5 the participant was warned and asked for confirmation earlier, at 2000 m before the exit and the ‘count down’ was shown from 1000 m before the exit. In both strategies, if the participant did not react to the first confirmation request, a second one was issued at 750 m before the exit.

    100%  The experimental setup of a large field operational test for cooperative driving vehicles at the A270.

    To be able to choose a suitable way of communicating with the driver through a HMI in the A270 experiments, several HMI alternatives are tested by means of a driving simulator.

    The chosen HMI design consists of a triangle which fills up with red when (more) deceleration is needed or a circle which fills up with green when (more) acceleration is needed, see left and right plot of Figure 3, respectively. The color signs are only shown when needed. As soon as no acceleration or deceleration is requested from the driver, i.e. a constant speed must be kept, the display is either a gray triangle or gray circle. When acceleration or deceleration gets more urgent an acoustic signal is added to the visual display saying “speed up” or “slow down”, respectively. The reason behind the sound is that more attention is attracted to the needed action from the driver and it gives the driver an extra motivation to follow up the advice.

    Experimented with possible ways to get the driver back in the loop.

     

    100% Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence.

    In a study by Brook-Carter et al. (2002), a red rectangle appeared on the simulator screen and the participant had to respond as quickly as possible by pressing the horn.

    In a driving simulator study by Ma (2006), participants were requested to press a button on the steering wheel when the navigation aid was activated, which occurred after about 9 min of driving.

    De Winter et al. (2014) found that drivers responded faster to arrow-shaped stimuli projected on the simulator screen during HAD as compared to manual driving

     

    Different ways are applied.”

    IMPACT-Digital Infrastructure

    Automated Vehicles, Are we ready ?nnChapter 3.1nnA common element in all levels of automation is safety-critical electronic control systems. There are voluntary industry standards, such as ISO 26262, which establish uniform practices for specific levels of safety integrity in complex embedded systems. In the United States, NHTSA has the regulatory responsibility for performance standards for vehicle systems or sub-systems that address a specific type of safety risk. For AVs, NHTSA is focusing on developing functional safety requirements as well as potential liability requirements in the areas of diagnostics, prognostics and failure response (fail safe) mechanisms (NHTSA, 2014). nn 

    60%nnThe Release of Autonomous Vehiclesnn21.3  Requirements for a Test ConceptnnIn order to discuss in the following section why full automation poses a particular challenge for safety validation, we will first describe the requirements for test concepts to assess safety. These are divided into effectiveness and efficiency criteria.nn21.5.1 Validity of the current test concept for autonomous drivingnnAt present, real driving is the most important method for the approval; the reason for this, in particular, is the validity combined with the justifiable economic overhead. However, along with the economic overhead, autonomous driving also presents a systematic challenge for the known methods. At present, real driving stands for driving in public road traffic with test drivers. The task of the test driver is to drive or supervise the vehicle in every situation in accordance with the task of the vehicle user. Transferred to autonomous driving, the use of a test driver in the driver’s seat would be non-real behavior of a user, as the user does not have to supervise the vehicle and the environment anymore and intervene.nnMotoring of the futurennPoint 64, page 27 in reportnn50%nnWitnesses discussed the research evidence for the effectiveness of different systems. Professor Sampson said that it was very difficult to research which technologies were most effective in terms of reducing accidents, because of the difficulties in running controlled trials of different features, with sufficient numbers of vehicles. Professor Carsten explained that the key struggle was with the continual monitoring and evaluation of technology, and developing an understanding of how casualty rates were affected over time by different technologies.nnHe explained that while it was statistically possible to show the safety benefits arising from car impact regulations, it was “really hard” to do this in relation to other safety approaches.

    50%nnROAD SAFETY WITH SELF-DRIVING VEHICLES: GENERAL LIMITATIONS AND ROAD SHARING WITH CONVENTIONAL VEHICLESnnChapter 3.2.1nnGomes(2014) argued that, “all 4 million miles of U.S. public roads will need to be mapped, plus driveways, off-road trails, and everywhere else you’d ever want to take the car” and this information would need to include “locations of street lights, stop signs, crosswalks, lane markings, and every other crucial aspect of a roadway.”

    70%nnState of Art on Infrastructure for Automated VehiclesnnChapter 6nnThis  section  summarizes,  based  on  insights  from  the current scientific literature, projects, test sites, and  initiatives,  the  implications  of  vehicle  automation  on  the  infrastructure  for  each  SAE  level  of automation (in each case assuming 100% penetration level). According to Shladover (31) level 5 will not be here until 2075, while level 3 is problematic because of the difficulty to attain drivers’ attention after  being  out  of  the  loop  and  because  some  automakers  simply  will  not  attempt  level  3.  However, level 4 automation will probably be realized within the coming decade. In Table 6 a first attempt was made to summarize the requirements from the physical infrastructure to facilitate vehicle automation, followed by Table 7 which summarizes the requirements from the digital infrastructure. These results should  be  considered  with  caution,  as  many  of  the findings  from  the  scientific  literature  were  not explicitly based on empirical data and results, but on experts’ opinions.nn 

    50% nnZoeken naar strepen op het asfaltnnPage 4 of document : Jene van der Heide, Senior advisor strategy and policy at Kadaster: “For mapmakers it is too expensive to make maps of the remote roads in the outskirts. And the government only collects information needed for the maintenance of such roads. If you drive into such an area, a self-driving car will probably ask for his driver to take the wheel over from him. It could take 10 years for this difference between the outside area and highway is eliminated. ‘

    IMPACT-Infrastructure

    Dutch: Rapport Zelfrijdende auto’s, verkenning van implicaties op het ontwerp van wegen

    Translated: Chapter 3.3
    -As a boundary of road lanes, physical marking is important, besides to the optional digital marking. This applies to all road sections. The marking must be clearly perceived by in-car sensors (cameras) and by the human driver at different weather and light conditions.
    -As the degree of automation increases, there are more and more vehicles that can drive on narrower lanes. However, drivers of vehicles that are not yet automated are benefiting from the current lane width (fear margin and exit). Thus, lanes cannot be made smaller everywhere. As an intermediate solution, smaller target lanes can be introduced for the higher SAE level vehicles.

    -Adjustments of the cross section and the berm direction (restraint strip, obstacle-free zone, emergency strips) can not be adjusted yet.

    Arc radians cannot be adjusted. However, it could be considered that in multi-lane arches the manually controlled vehicles are only allowed in the outer strip / strips. The inner strip / strips can be reserved for automatic vehicles that can optimize their speed on the infrastructure features and preferences of the occupants. There is a risk that manually controlled vehicles will imitate the behavior of automotive vehicles, which could lead to overturning them too fast. The short driving times of automotive vehicles could also be mimicked by human drivers. This also applies to other road sections.

    -Overall, the mix of different vehicle types can initially give some unpredictable traffic image. This in particular affects the dimensioning of exchange points. (traffic entering an exiting, weave, intersection, roundabout). The mix of vehicles of different SAE levels can provide an interaction between the different types of vehicles that go against the intuition of human drivers. Automated vehicles behave differently than drivers of non-automated vehicles, or drivers of vehicles with lower SAE levels. Exchange in some places may be too complex for automated vehicles, who are not yet communicating and need more time, due to the greater safety margins than those held by human drivers and braking early. This may lead to a broader dimensioning of exchange points (entry and exit strips, weave). This is consistent with the practice observation that ACC in its current form leads to greater follow-up distances.

    For underlying roads, especially the interaction with slow traffic (cyclists and pedestrians) causes many dilemmas. As a result, the situation becomes much more complex, and no (safe) solutions are available at this time. On area-bound roads with separate traffic flows, this also applies (at intersections and roundabouts).

    -When traffic is mixed crossroad design and priority rules must still be retained as usual. The automated vehicle has to adapt itself to the human drivers so that confusion is avoided with drivers of non-automatic vehicles. The automated vehicle may need more time to estimate the situation at a crossroads, if no vehicles are available to communicate with in the neighborhood

    – Visibility on a roundabout is not a problem for automated vehicles if it can communicate with other traffic, this causes a problem for human drivers though

    – (truck) platooning still brings the necessary questions if the aim is to be able to platoon almost continuously (so no splitting at every node) to achieve the benefits of platooning. From the above, the impression is that the possible road design implications in the mixed traffic situation are likely to be rather limited (either you can not change road design as long as there is mixed traffic). In the case of mixed traffic, no change in design are possible, based on what human drivers need to drive safely, smoothly and comfortably. Only on high lane /capacity roads it could be considered to reserve a part of these lanes to automated vehicles, and to redesign this road section (separation in the cross profile of automated and non-automated).

    Smart Infra, Eerste schetsonderzoek naar level 4 snelwegen en kruispunten voor zelfrijdende auto’s
    Chapter 5
    For the transit phase, both the automated conventional vehicles use the same lane. As long as conventional vehicles use lanes, they will determine the design criteria to maintain road safety. In the transitional phase, it is therefore unlikely to adapt the design of motorways. For a separate transition, a target group on the left side of the lane will be designated for automated vehicles. In case the target strip is used as an additional strip, the remaining strips will have to be narrowed and the emergency lane might possibly be sacrificed. Both measures lead to a deviation from the current directive (ROA 2014) and may have a negative effect on the road safety of especially conventional vehicles. The extra strip however, increases the total capacity of the road.

     

    “Cars driving from work to home can be  designed smaller. Since 80% of these rides hold only one passenger. Lanes that are for AV use only can change their driving direction dynamic, to improve road capacity inbound or outbound traffic. Found in Dutch on (p.54): Verkenning technologische innovaties in de leefomgeving

    Another deployment scenario for automated driving involves implementing transportation paradigms that provide slow-moving passenger vehicles, for example in urban areas. Consumers could summon such vehicles using a smartphone app and ride them over relatively short distances (see the use case “vehicle on demand”). In particular, companies from unrelated sectors can use image processing, object recognition, and route planning systems—which are already in widespread modular use—to implement transportation models with higher-order automation within a limited geographical range. The arrangements often proposed for market introduction are slow-moving and limited-area vehicles intended to serve what is known as the “first or last mile,” complementary to private automobiles or public transportation. To name one concrete example, these types of solutions could be used to reach bus and urban rail networks in areas where a regular schedule is not feasible due to inadequate infrastructure or financial limitations. Found on (p.199-200): https://www.dropbox.com/s/91n2z7i19wfgzu6/Beiker%2C%20S.%20%282016%29%20Deployment%20Scenarios%20for%20Vehicles%20with%20Higher-Order%20Automation.pdf?dl=0nn Note Joop: In the Netherlands there are tests concerning a connection with the station Ede and the Campus of Wageningen (WEPODS). In 2018 there will be tests with the WEPOD to the airport Weeze in Germany. There are also some test in the region MRDH near Airport Rotterdam.

    “Automated driving, with its minimal space requirements and rather equal speed levels, could at least double the existing average road infrastructure capacity. “nnFound on (p.380): Autonomous Vehicles and Autonomous Driving in Freight Transportnn 

    80%nnAutomated Vehicles. The Coming of the Next Disruptive TechnologynnBenefits page 17 reportnnWith AVs, the demand for parking will decrease substantially because an AV can relocate itself to an area of free parking. Or, as an automated taxi, it can pick up its next ride. In some cases, a commuter can send the car home for his/her spouse to use. nn nnBesides changes in parking spots Urban land use is expected to change as well with the implementation of AV’s. This Autonomous Driving and Urban Land Use report discusses the possible effects.

    IMPACT-Traffic-Handling

    “Cars driving from work to home can be  designed smaller. Since 80% of these rides hold only one passenger. Lanes that are for AV use only can change their driving direction dynamic, to improve road capacity inbound or outbound traffic. Found in Dutch on (p.54): Verkenning technologische innovaties in de leefomgeving

    “According to (Anderson, et al. 2014) Self-propelled vehicles could considerably upsurge access and movement across a variety of populations presently incapable or not permitted to use conventional automobile. These include the incapacitated, older people, and children of age 16 or less. The most promising advantages would be personal independence, increased sociability, and access to vital services. Level 4 automation is expected to provide mobility and access at reduced cost when compared to the current system which provides mobility services for disabled for 14 to 18 percent of their budgets in the U.S. “nnFound on (p.42): Impact of Autonomous Vehicles on Urban Mobilitynn“This type of combined and improved transport systems come with many advantages like they would reduce the necessity for roads and parking; lessen congestion, air pollution and greenhouse gas emissions; would support the optimization of capitals used for transportation; and upsurge the living standards in the region. “nnFound on (p.39): Impact of Autonomous Vehicles on Urban Mobility nn“Considering that off-street parking represents 50 000 spots in the baseline case and that the most parking- intensive scenario (car sharing without public transport) would require 25 621 spots, on-street parking spots could be totally removed from the streets, whatever the scenario considered. This would allow the reallocation of 1 530 000 m2 to other public uses2, equivalent to almost 20% of the kerb-to-kerb street area in Lisbon or 210 football fields. This freed-up space could be dedicated to non-motorised transport modes, delivery bays, parklets or other recreational and commercial uses. “nnFound on (p.26): Urban Mobility Upgrade

    “According to (Anderson, et al. 2014) Self-propelled vehicles could considerably upsurge access and movement across a variety of populations presently incapable or not permitted to use conventional automobile. These include the incapacitated, older people, and children of age 16 or less. The most promising advantages would be personal independence, increased sociability, and access to vital services. Level 4 automation is expected to provide mobility and access at reduced cost when compared to the current system which provides mobility services for disabled for 14 to 18 percent of their budgets in the U.S. “Found on (p.42): Impact of Autonomous Vehicles on Urban Mobility Stress, productivity, road capacity, energy, emissions:“Potentially, autonomous vehicles could significantly reduce stress levels and offer us the capability to rest – or work – while traveling. Driverless cars could also provide independent mobility for non-drivers, increase road capacity and reduce traffic congestion. They could additionally reduce parking costs, accidents, offer energy conservation, emissions reductions as well as more scope for vehicle sharing. “nnFound on (p.037): Ready or WaitingnnKiM recently published a second study around this subject. See library.

    The opinions are divided, depending on different assumptions about the future: nnProbable nothing imported in explained in the following article. :nn“Less likely, even if repeatedly cited by the scientific community, is the conversion of the entire vehicle fleet into vehicles that are on the road on a sharing basis or operated by public transport providers. There are currently no indications that private cars are losing any of their attraction.” nnScenario with individual Public TransportnnOne possible first step in individualizing public transport could be company buses, such as the so-called Google Bus, equipped with WiFi access and operating in and around San Francisco, which brings the company’s employees to work. In this case, a specific community gets together in a communal shuttle. Comparable concepts, albeit in manifold varieties, are conceivable and appear particularly attractive when based on autonomously driven vehicles. “nnFound on (p. 180 & 186):nnhttps://www.dropbox.com/s/i3wankjtjm4y1xc/Lenz%2C%20B.%20%26%20Fraedrich%2C%20E.%20%282016%29%20New%20Mobility%20Concepts%20and%20Autonomous%20Driving%20-%20The%20Potential%20for%20Change.pdf?dl=0nn

    IMPACT-Traffic-Safety

    Automated Vehicles, Are we ready ?nnChapter 3.1nnA common element in all levels of automation is safety-critical electronic control systems. There are voluntary industry standards, such as ISO 26262, which establish uniform practices for specific levels of safety integrity in complex embedded systems. In the United States, NHTSA has the regulatory responsibility for performance standards for vehicle systems or sub-systems that address a specific type of safety risk. For AVs, NHTSA is focusing on developing functional safety requirements as well as potential liability requirements in the areas of diagnostics, prognostics and failure response (fail safe) mechanisms (NHTSA, 2014). nn 

    60%nnROAD SAFETY WITH SELF-DRIVING VEHICLES: GENERAL LIMITATIONS AND ROAD SHARING WITH CONVENTIONAL VEHICLESnnChapter 3/ ConclusionnnSelf-driving vehicles could compensate for some but not all crashes caused by other traffic participants (Pedestrian error could be compensated by AV). Lighting failures might turn out to be irrelevant to safety from the perspective of being able to control one’s vehicle at night, because self-driving vehicles might not rely on visual input. / (1) The expectation of zero fatalities with self-driving vehicles is not realistic. (2) It is not a foregone conclusion that a self-driving vehicle would ever perform more safely than an experienced, middle-aged driver. (3)During the transition period when conventional and self-driving vehicles would share the road, safety might actually worsen, at least for the conventional vehicles.nnSafety Benefits of Automated Vehicles: Extended Findings from Accident Research for Development, Validation and Testingnn17.4  Significance of Possible Predictions based on Accident Datann 

    70%nnSafety Benefits of Automated Vehicles: Extended Findings from Accident Research for Development, Validation and Testingn17.1 introductionnn→ report goes into more detailnFor testing methods in order to develop and validate safe automated vehicles with reasonable expenditure, the author recommends combining area-wide traffic, accident, weather, and vehicle operation data as well as traffic simulations. Based on these findings, a realistic evaluation of internationally and statistically relevant real world traffic scenarios as well as error processes and stochastic models can be analyzed (in combination with virtual tests in laboratories and driving simulators)to control critical driving situations in the future. nnSelf-Driving Regulation, Pro-Market Policies Key to Automated Vehicle Innovationn50%nOne important challenge, which is expected to be met by late 2014 or early 2015 , is providing sufficient evidence that road – tested autonomous vehicles are in fact safer than manually driven vehicles. As Bryant Walker Smith of Stanford Law School has noted, a high degree of statistical confidence must be reached in order for automakers and component developers to begin scaling up technology deployment beyond testing.nnGoogle’s self- driving cars have logged over 500,000 miles on U.S. public roads to date. To demonstrate their safety over manually driven vehicles with 99 percent confidence, Google will need to log approximately an additional 200,000 miles of crash-free automated driving (see Table 2).

    70%nnAutomated Vehicles, Are we ready ?nnChapter 3.3nnAVs are capable of providing large amounts of data that could assist investigation in case of a crash. By recording the actions and forces involved in the minutes before and after a crash, they may help determine the cause of the crash and assist in resolving any liability dispute.nnMotoring of the futurennPoint 33, page 15 in reportnnTelematics also known as ‘black boxes’ monitor the location of a driver and driving performance.nnSafety Benefits of Automated Vehicles: Extended Findings from Accident Research for Development, Validation and TestingnnHowever, a safety prognosis of highly or fully automated vehicles depends on assumptions, as so far no series applications of such features exist. For testing methods in order to develop and validate safe automated vehicles with reasonable expenditure, the author recommends combining area-wide traffic, accident, weather, and vehicle operation data as well as traffic simulations.nnTwo questions discussed in paper:nnn–What significance do analyzes and findings from road-accident research hold for the introduction of automated vehicles?nn–How can the potential safety benefits of automated vehicles be established?nnThe validity of accident data regarding potential safety benefits varies considerably depending on the collection method.

    LEGAL-Admissionontheroad

    0%n n This aspect is not mentioned in the literature, and perhaps a more technical question.n n & Nbsp;

    50%n n (Motoring of the future) The requirements for vehicles should follow the newest technologied. This makes it difficult to establish a framework for the vehicle of tomorrow.

    0%n n The legal position of the government with defective automated vehicles is not named.

    75%n nFrom: (The pathway to driverless cars, a code of practise for testing) “It is expected that automated driving systems will rely on the interaction and correct operation of several computers and electronic control modules. It will be important that:n

      n

    • Software levels and revisions running on each vehicle to be tested are clearly documented and recorded.
    • n

    • All software and revisions have been subjected to extensive and well documented testing. This should typically start with bench testing and simulation, before moving to testing on a closed test track or private road. Only then should tests be conducted on public roads or other public places.”
    • n

    nUit: (Products Liability and Driverless Cars): ” Common sense would hold that, if an original manufacturer in no way participates in or promotes the post-sale installation of autonomous vehicle technology manufactured by a third party, the original manufacturer should not be liable for alleged defects in that technology. Unfortunately, some of the case law relating to liability for third-party conversions in other contexts doesn’t necessarily support this common sense conclusion.”

    0%n n There is no mention of defined security levels. This may indicate that no standards have been developed.

    Legal-Aspects

    25%n nFrom (Automated Vehicles are probably legal in the United States): “The rules of the road as codified assume human judgment, and the rules of the road as observed reflects that judgment. These dependencies may complicate the lawful operation of automated vehicles. “n n “These references to reasonableness, prudence, practicability, and due care demonstrate That the law accepts risk at a level certainement, They neither prescribe nor Specify this level for a base-determining it. “n n “Application of these laws to automated vehicles may present both design challenges and liability concerns.”n n & Nbsp;n

      n

    • What strategies regulations are available and what do we use? n n 75%n n Several dilemmas are presented in (Automated and Autonomous Driving: Regulation under uncertainty)n
        n

      1. Treat automated vehicles specifically or generally?
      2. n

      3. Let policy lead or lag technology?
      4. n

      5. Privilege uniformity or flexibility?
      6. n

      7. Emphasise ex-ante or ex-post regulation?
      8. n

      n

        n

      • How to broaden the perspective of the rules without becoming too big and too complex? (For example one or more frameworks using use cases, deployment in specific situations). n n 0%n n This is not mentioned in the literature.n n & Nbsp;n 
          n

        • What relationships in the existing regulations are relevant for automatic driving and in which rule sets, eg: The vehicle (admission), traffic (safety, flow), the infrastructure and its management in the public space, mobility, transport of persons or goods, data and privicy, data infrastructures, (geo) information systems and information management, eurpese regulation / harmonization. n n 10%n n (Regulation and the Risk of Inaction) appoint the legal complexity. It can be said that each of the relationships is relevant.
    Category: Legal-Aspects

    0%n n The answer can not be found in the legal domain, but should be found in the technical and human factors domain.

    Category: Legal-Aspects

    25%n nIn (How Governments Can Promote Automated Driving) a chapter is devoted to increasing social acceptance. In US states, where experimenting is done with automatic vehicles, the government does this asset. In the literature the risk of the citizen is not mentioned.

    Category: Legal-Aspects

    75%n n From (Fundamental and Special Legal Questions for Autonomous Vehicles)n n“If the introduction of autonomous driving is taken into account against this backdrop, it may be assumed that the interests would remain unchanged—at least for the mixed traffic scenario of autonomous and driver-controlled vehicles on which this study is based (see Chap.2). A change could occur if—due to a very high degree of automated-system mastery of the automated control risk associated with a violation—the described consideration finds that the violation does not result in an increased risk. This would, of course, call into question the very rationale for the rule itself: “Violation” of the rule would then not actually produce any disadvantage in the context of mixed traffic. It is also conceivable, however, that autonomous driving will lead to a need forconsiderably more detailed traffic rules which are significantly less flexible (which would in turn raise the question of societal acceptance again). Such a restriction of the existing situation would in turn require a more detailed catalog of requirements in terms of the technical control capability of autonomous vehicles as soon as they become foreseeable from a technical standpoint. This would make it possible to identify consequences, including in the case of violations, in much greater detail than is currently possible.”

    Category: Legal-Aspects

    0%n n The answer can not be found in the legal domain, but should be found in the technical and human factors domain.

    Category: Legal-Aspects

    LEGAL-Liability

    10%n n This is nowhere suggested in the literature. Therefore, it seems an unlikely option.

    Category: LEGAL-Liability

    25%n n (Autonomous Vehicle Liability-Application or Common Carrier Liability) shows some examples, such as the automatic pilot of aircraft and ships, and the first elevators.

    Category: LEGAL-Liability

    50%n n (Products Liability and Driverless Cars) (Sit, Stay, Drive: The Future of Autonomous Car Liability) (Product Liability Issues in the US and Associated Risk Management)n n Most literature implies and assumes that product liability will become the norm. Conversely, the paper (Sit, Stay, Drive: The Future of Autonomous Car Liability) sketches a situation in which the owner is responsible and takes out an insurance.

    Category: LEGAL-Liability

    90%n (Aansprakelijkheidsaspecten van zelfrijdende auto’s Een verkennende analyse), (German Federal Highway Research InstituteVehicle Automation: Definitions, legal aspects, research needs), (Als de auto autonoom wordt.), (Autonomous Vehicle Liability—Application of Common Carrier Liability), (The Cost of Self-Driving Cars: Reconciling Freedom and Privacy with Tort Liabilityt in Autonomous Vehicle Regulation), and more.n n The consensus is that an illegal act by an automated vehicle is a case of a defective product. The producer is liable.

    Category: LEGAL-Liability

    25% From (Fundamental and Special Legal Questions for Autonomous Vehicles): “Insofar as a need for regulation of autonomous vehicles does not emerge naturally from the novel machine-based automation risk, it would seem that at least in terms of the use of suitable functions in mixed traffic relating to error compensation and communication capabilities (see Sect. 25.5.5) of autonomous vehicles, a need for legal regulation seems evident.”

    Category: LEGAL-Liability

    LEGAL-Privacy

    25%n n Camera footage is not separately discussed. General insights about data could also be applied to camera footage.

    Category: LEGAL-Privacy

    50%n n From (Opportunities and Risks Associated with Collecting and Making Usable Additional Data)n n“Limiting access rights and encryption are typical instruments of information security. Limiting access rights is also mentioned under the “Information Security” principle of ISO/IEC 29100. It follows the concept of “need-to-know”, limiting access to PII to those individuals who require such access to perform their duties, and limiting the access of those individuals to only that PII which they require to perform their duties. Access rights can be defined by defining exactly which entity can access which PII. This asks for a fine-grained specification of the system, and can best be achieved if privacy is already considered during the design phase, e.g. when designing which data are collected by the vehicle and for which application they are needed.”

    Category: LEGAL-Privacy

    75%n n This is not enshrined in law, but depends on the agreements concluded by a consumer with for example, the car manufacturer and the insurer. Examples of these are in (Opportunities and Risks Associated with Collecting and Making Additional Usable Data)

    Category: LEGAL-Privacy

    TECHNICAL-(Cyber)Security

    90% Adviesrapport Cybersecurity Autonoom rijdende voertuigen , Fox IT (2014)nThis report gives insight in the vulnerabilities of mordern vehicles, risk at the introduction of autonomous driving and possible measures to prevent these risks.nEven tough the threat is small, it is very important to take good measures. For the short term, six general measures are proposed that make it possible to get a view on the security of systems for autonomous vehicles in the short term and in this way improve the safety and security of driving in those. nThese 6 measures focus on research of autonomous vehicles on vulnerabilities, solving these and being prepared for an attack through a found vulnerability. nIn view of the expectation that cybercrime for autonomous vehicles will grow strongly and not all problem can be predicted in advance, it is advisable in the long term to seek a framework in which measures can be developed.nn60% Comprehensive Experimental Analyses of Automotive Attack SurfacesnProvides insight in the various ways of digital hacking of vehicles.n‘’We discover that remote exploitation is feasible via a broad range of attack vectors (including mechanics tools, CD players, Bluetooth and cellular radio), and further, that wireless communications channels allow long distance vehicle control, location tracking, in-cabin audio exfiltration and theft.’’nn50% Security Challenges for Cooperative andInterconnected Mobility SystemsnnIdentifies and quantifies the risks of Interconnected Mobility Systems.nn‘’The biggest security risk factors foreseen are application data integrity validation, the usage of insecure position information and systems that are currently not secure by design. These risk factors will have to be addressed in the coming years, to pave the road for successful introduction of cooperative and interconnected mobility systems’’

    Our annual knowledge report reports on this. It indicates to which knowledge questions answers and research have become available. In December, we will put the subjects and knowledge questions for research and trials into the coming year. Currently, the priorities in the list of knowledge questions (AR + C-ITS) are being worked on by, among others, IenW, RWS, Knowledge Institutions and Provinces, Cities, regions and pilot projects.

    On this site you will also find an overview of relevant conferences and events and a collection of films and webinars via the menu. News and current developments are maintained by us through the library and twitter feed (#KARNL). Every week, a lot of knowledge and material is added to the collection, in all knowledge areas.