scholarly journals Car-following behavioural adaptation when driving next to automated vehicles on a dedicated lane on motorways: A driving simulator study in the Netherlands

2021 ◽  
Vol 78 ◽  
pp. 119-129
Author(s):  
Mathijs Schoenmakers ◽  
Dujuan Yang ◽  
Haneen Farah
Keyword(s):  
The Netherlands ◽  
Driving Simulator ◽  
Automated Vehicles ◽  
Behavioural Adaptation ◽  
Car Following ◽  
Simulator Study

scholarly journals Do drivers change their manual car-following behaviour after automated car-following?

2020 ◽  
Author(s):  
Tyron Louw ◽  
Rafael Goncalves ◽  
Guilhermina Torrao ◽  
Vishnu Radhakrishnan ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Cruise Control ◽  
Automated Driving ◽  
Behavioural Adaptation ◽  
Car Following ◽  
Time Headway ◽  
Driving Behaviour ◽  
Driving Assistance ◽  
Change In Mean ◽  
Lead Vehicle

There is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control.

Analyses on the heterogeneity of car-following behaviour: evidence from a cross-cultural driving simulator study

2020 ◽  
Vol 14 (8) ◽  
pp. 834-841
Author(s):  
Qian Cheng ◽  
Xiaobei Jiang ◽  
Wuhong Wang ◽  
André Dietrich ◽  
Klaus Bengler ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Cross Cultural ◽  
Car Following ◽  
Simulator Study

Exploring Driver’s Deceleration Behavior in Car-Following: A Driving Simulator Study

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Junyu Hang ◽  
Xuedong Yan ◽  
Ke Duan ◽  
Xiaomeng Li ◽  
Jingsi Yang
Keyword(s):  
Driving Simulator ◽  
Car Following ◽  
Simulator Study

scholarly journals Do drivers change their manual car-following behaviour after automated car-following?

2020 ◽  
Author(s):  
Tyron Louw ◽  
Rafael Goncalves ◽  
Guilhermina Torrao ◽  
Vishnu Radhakrishnan ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Cruise Control ◽  
Automated Driving ◽  
Behavioural Adaptation ◽  
Car Following ◽  
Time Headway ◽  
Driving Behaviour ◽  
Driving Assistance ◽  
Change In Mean ◽  
Lead Vehicle

AbstractThere is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving-related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car-following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control.

Car-Following Model Calibration Based on Driving Simulator Data to Study Driver Characteristics and to Investigate Model Validity in Extreme Traffic Situations

2021 ◽  
pp. 036119812110326
Author(s):  
Moritz Berghaus ◽  
Eszter Kallo ◽  
Markus Oeser
Keyword(s):  
Driving Simulator ◽  
Driver Behavior ◽  
Field Studies ◽  
Driving Behavior ◽  
Model Parameters ◽  
Prediction Errors ◽  
Trajectory Data ◽  
Car Following ◽  
Model Validity ◽  
Simulator Study

In this paper we use traffic data from a driving simulator study to calibrate four different car-following models. We also present two applications for which the calibration results can be used. The first application relied on the advantage that driving simulator data also contain information on driver characteristics, for example, age, gender, or the self-assessment of driver behavior. By calibrating the models for each driver individually, the resulting model parameters could be used to analyze the influence of driver characteristics on driver behavior. The analysis revealed that certain characteristics, for example, self-identification as an aggressive driver, were reflected in the model parameters. The second application was based on the capability to simulate dangerous situations that require extreme driving behavior, which is often not included in datasets from real traffic and cannot be provoked in field studies. The model validity in these situations was analyzed by comparing the prediction errors of normal and extreme driving behavior. The results showed that all four car-following models underestimated the deceleration in an emergency braking scenario in which the drivers were momentarily shocked. The driving simulator study was validated by comparing the calibration results with those obtained from real trajectory data. We concluded that driving simulator data were suitable for the two proposed applications, although the validity of driving simulator studies must always be regarded.

scholarly journals Human Performance in Critical Scenarios as a Benchmark for Highly Automated Vehicles

2021 ◽  
Author(s):  
Laura Quante ◽  
Meng Zhang ◽  
Katharina Preuk ◽  
Caroline Schießl
Keyword(s):  
Inflection Point ◽  
Human Performance ◽  
Driving Simulator ◽  
Avoidance Performance ◽  
Safety Performance ◽  
Regression Curve ◽  
Automated Vehicles ◽  
Time To Collision ◽  
Simulator Study ◽  
Method Of Constant Stimuli

AbstractBefore highly automated vehicles (HAVs) become part of everyday traffic, their safety has to be proven. The use of human performance as a benchmark represents a promising approach, but appropriate methods to quantify and compare human and HAV performance are rare. By adapting the method of constant stimuli, a scenario-based approach to quantify the limit of (human) performance is developed. The method is applied to a driving simulator study, in which participants are repeatedly confronted with a cut-in manoeuvre on a highway. By systematically manipulating the criticality of the manoeuvre in terms of time to collision, humans’ collision avoidance performance is measured. The limit of human performance is then identified by means of logistic regression. The calculated regression curve and its inflection point can be used for direct comparison of human and HAV performance. Accordingly, the presented approach represents one means by which HAVs’ safety performance could be proven.

scholarly journals Repeated Usage of an L3 Motorway Chauffeur: Change of Evaluation and Usage

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 114 ◽  
Author(s):  
Barbara Metz ◽  
Johanna Wörle ◽  
Michael Hanig ◽  
Marcus Schmitt ◽  
Aaron Lutz
Keyword(s):  
Driving Simulator ◽  
Automated Driving ◽  
Driving Function ◽  
Behavioural Adaptation ◽  
The Road ◽  
Driving Time ◽  
Level 3 ◽  
Simulator Study ◽  
First Contact ◽  
Highly Automated Driving

Most studies on users’ perception of highly automated driving functions are focused on first contact/single usage. Nevertheless, it is expected that with repeated usage, acceptance and usage of automated driving functions might change this perception (behavioural adaptation). Changes can occur in drivers’ evaluation, in function usage and in drivers’ reactions to take-over situations. In a driving simulator study, N = 30 drivers used a level 3 (L3) automated driving function for motorways during six experimental sessions. They were free to activate/deactivate that system as they liked and to spend driving time on self-chosen side tasks. Results already show an increase of experienced trust and safety, together with an increase of time spent on side tasks between the first and fourth sessions. Furthermore, attention directed to the road decreases with growing experience with the system. The results are discussed with regard to the theory of behavioural adaptation. Results indicate that the adaptation of acceptance and usage of the highly automated driving function occurs rather quickly. At the same time, no behavioural adaptation for the reaction to take-over situations could be found.

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