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 email@example.com 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:
“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 .”nnFound on (p.690): Consumer Perceptions of Automated Driving Technologies: An Examination of Use Cases and Branding Strategiesnn
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?
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.
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)
Use of signaling form cars, for example direction indicator
How much time will it cost to get the driver back in the loop?/How much time will a driver need to get back in the loop after a long ride of automated driving on the highway?
Did same research but with little participants(16) and in a simulator.
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.
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.
S.Hill presentation gives results of the research about time of out-of-loop to in-to-loop.
- 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.
Different experminents, with different times as answer. The answers are divided in big pieces of tekst in the article. “
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.
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.
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.”
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.