Benodigde kennis

De benodigde kennis wordt in samenwerking bepaald tijdens werkbijeenkomsten en Ronde Tafels. Dit leidt tot kennisvragen die worden beheerd door het Ministerie van Infrastructuur en Milieu (DGB).

De onderstaande vragen zijn al deels beantwoord. De antwoorden staan in de documenten uit de collectie en zijn hier verzameld. (stand per maart 2017) Voor meer vragen en antwoorden: zie het Kennisjaarverslag.

HUMAN BEHAVIOUR-Gebruikersgedrag

“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].”

Gevonden in (p.690): Consumer Perceptions of Automated Driving Technologies: An Examination of Use Cases and Branding Strategies

 

50% Behavioural adaptation, risk compensation, risk homeostasis and moral hazard in traffic safety

The researchers concluded that the observed behaviours are associated with increased accident likelihood and that one should be cautious about the potential safety of the ACC systems. The results showed that drivers using ACC react too late and collide more often with a stationary queue than unsupported drivers. The researcher suggests that these collisions are due to the driver’s expectations that the ACC system would cope with the situation. Although informed about ACC limitations, drivers seem to have problems to identify situations requiring them to take over control. Similar conclusions were drawn in a study of Stanton et al. (1997, quoted in Rudin-Brown and Parker, 2004).

In an emergency stop scenario, the average maximum braking was larger and the average minimum time headway was smaller when driving with ACC. The type of driving style group made little
difference to these behavioural adaptations. The researchers concluded that the observed behaviours are associated with increased accident likelihood and that one should be cautious about the potential safety of the ACC systems.

The results show that the drivers reacted more slowly and less often within

a safe time period (approximately 33% less often) on this task when using ACC. This effect was particularly pronounced in ‘high sensation seekers’. Furthermore, ACC was also associated with impaired lane-keeping performance and high sensation seekers were more deviating within the lane then the other test persons.

50% Misconceptions and self-reported behavioral adaptations associated with advanced in-vehicle systems: lessons learned from early adopters.

Gevaren van het gebruik van ACC

While nearly all drivers reported reading all or some part of the owner’s manual relating to the ACC system, many held misconceptions about the functional capabilities of the system. For example, most owners mistakenly believed the ACC system would react to a stopped in-path vehicle, and many were not aware that the system provided an approach warning feature that alerts the driver when manual intervention is required in situations where the system’s braking authority is exceeded. Of greater concern are drivers who think that the warning feature operates all the time, when in fact it does not. Over 6% of drivers in our sample were under the mistaken impression that the approach warning feature is active in their vehicles even when the ACCsystem is disengaged; these individuals are assuming a greater level of protection than the system actually provides. Again, this misinterpretation of the system’s capability was not moderated by experience with the system.

Of some concern is the finding that a substantial percentage of system owners (27%) were unsure of the underlying basis for how the system triggered the warning (distance or a combination of speed and distance). Inexperienced users, those with less exposure to the park aid system, were more likely to incorrectly assume that the system adjusts the timing of the warning based on both speed and distance to the obstacle, rather than using a fixed warning timing based on distance alone. Nearly twice as many “low experience” users (22%) were operating under this false assumption compared to their more experienced counterparts (11%). The vast majority of drivers were also unaware of the system’s functional speed limitations; 67% believed that the park aid system operates under any speed when backing (most systems only operated at speeds under 6 mph). Experience with the system also did not appear to improve understanding of the system’s functional speed range.

 

Nevertheless,misconceptions are common, suggesting that drivers need to be better educated about the system’s capability and limitations. Some form or degree of driver behavioral adaptation was reported to have occurred for each of the systems examined (Llaneras, 2006). Despite access to a wide array of information about their in-vehicle system, responses to knowledge-based questions about the systems themselves suggest that key information was not necessarily acquired or understood by a large number of drivers. Many drivers held misconceptions about the performance capabilities of their advanced systems. For example, 99% of ACC system owners did not know that the system ignores stopped vehicles. The fact that the system ignores stopped or slow-moving vehicle is available in the owner’s manuals for all of the ACC systems reviewed, yet drivers were not aware of this important operational characteristic. Similarly, 41% of park aid system owners did not know that the system warning is tied solely to the distance to objects and does not take into account their closing speed. This suggests that drivers’ mental models of how these systems function and perform do not always match reality, and additional efforts are needed to increase driver understanding of how these systems operate. This is particularly important for safety-related misconceptions.

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.

50%

A Proposed Psychological Model of Driving Automation

Niet perse voor het behalen voor bepaalde doelen, maar die invloed hebben op het gebruik van de zelfrijdende auto

From the review of the literature, we believe that the interdependency between
the psychological concepts underrepresented. The mental model that a user develops
about a system is critical to performance and operations with that system. Given the
previous review of psychological factors associated with the use of automation, there are
some obvious (and some not-so-obvious) interrelations between the variables. From the
literature, it would seem that mental workload plays a central role in the relationship.
For instance, it is apparent that high workload in the form of traffic congestion can
increase stress (Wilson and Rajan, 1995), but there is some evidence that this
relationship is bi-directional. Matthews and Desmond (1995a; 1997) provide evidence
for the mechanism behind this relationship, and from this there are two novel yet logical
conclusions relating workload to stress. The first is that stress can affect performance in
low as well as high workload conditions. The second is that the effort involved in coping
with stress actually adds to the task demands.


Hieronder een lijst met een aantal documenten die enkel betrekking hebben op het subdomein. Er zijn nog meer documenten gerelateerd aan dit subdomein maar deze zijn ook gelinkt aan andere subdomeinen. De volledige collectie beschikbare documenten in onze online bibliotheek (catalogus en dropbox) 

Titel/Title Auteur(s)/Author(s) Product van/Produced by Opdrachtgever product/Product Requested by Publicatiedatum/ Publication date
AUTOMATIC DRIVER CHARACTERISTICS ESTIMATION USING DRIVING SIGNALS Kaminuma Atsunobu Nankaku Yoshihiko Nagoya Institute of technology Nissan Research Center, Nissan Motor. Co,. LTD 3-12-2015
Rijtaakindicatoren voor C-ITS-projecten Ilse Harms, Connecting Mobility, Matthijs Dicke, DITCM Innovations. Review Diana Vonk Noordegraaf, DITCM Innovations. Advies: Isabel Wilmink, TNO, Bert van Velzen, Grontmij, Jeroen Hogema, TNO Connecting Mobility Connecting Mobility 2-10-2016
Automated Vehicles and Automated Driving from a Demand Modeling Perspective Rita Cyganski German Aerospace Center (DLR), Institute of Transport Research Springer Berlin Heidelberg 22/05/2016
Towards a theory of situation awareness in dynamic systems. Mica Endsley Texas Tech University Texas Tech University ??/03/2015
A Proposed Psychological Model of Driving Automation Stanton, Young Brunel University Brunel University ??/01/2000
Risk homeaostasis theory: an overview Gerald Wilde Univeristy of Queens, Ontario Univeristy of Queens, Ontario ??/06/1998
Behavioural adaptation, risk compensation, risk homeostasis and moral hazard in traffic safety Vrolix, K Universiteit Hasselt Steunpunt Verkeersveiligheid ??/09/2006
Behavioural effects of Advanced Cruise Control Use – a meta-analytic approach N. Dragutinovic, Karel A. Brookhuis , Marjan P. Hagenzieker and Vincent A.W.J. Marchau University of Groningen SWOV 03-04-2005