Estimation of Controllability Based on Driver Behavior - A Case of Insufficient Brake-Assist Force

2014 ◽  
Author(s):  
Maki Kawakoshi ◽  
Takanobu Kaneko ◽  
Toru Nameki
Keyword(s):  
Driver Behavior ◽  
Brake Assist

Passenger Influence on Driver Behavior

2006 ◽  
Author(s):  
Hilary M. Anton-Stang ◽  
Sharan E. Gay ◽  
Krista B. Kumley ◽  
Tressa Mallomo ◽  
Stace Nichols ◽  
...  
Keyword(s):  
Driver Behavior

Impulsive Driver Behavior Scale--Chinese Version

2020 ◽  
Author(s):  
Yan Ge ◽  
Weina Qu ◽  
Mu Zhou ◽  
Türker Özkan ◽  
Pınar Bıçaksız ◽  
...  
Keyword(s):  
Driver Behavior ◽  
Chinese Version ◽  
Behavior Scale

Understanding Driver Behavior after Concussion: A Machine-Learning Approach

2020 ◽  
Vol 64 (1) ◽  
pp. 1911-1915
Author(s):  
Maryam Daniali ◽  
Dario D. Salvucci ◽  
Maria T. Schultheis
Keyword(s):  
Machine Learning ◽  
Task Performance ◽  
Verbal Memory ◽  
Driving Simulator ◽  
Cognitive Impairments ◽  
Driver Behavior ◽  
Memory Task ◽  
Learning Approach ◽  
Behavioral Task ◽  
Machine Learning Approach

Concussions are common cognitive impairments, but their effects on task performance in general, and on driving in particular, are not well understood. To better understand the effects of concussion on driving, we investigated previously gathered data on twenty-two people with a concussion, driving in a virtual-reality driving simulator (VRDS), and twenty-two non-concussed matched drivers. Participants were asked to per-form a behavioral task (either coin sorting or a verbal memory task) while driving. In this study, we chose a few common metrics from the VRDS and tracked their changes through time for each participant. Our pro-posed method—namely, the use of convolutional neural networks for classification and analysis—can accu-rately classify concussed driving and extract local features on driving sequences that translate to behavioral driving signatures. Overall, our method improves identification and understanding of clinically relevant driv-ing behaviors for concussed individuals and should generalize well to other types of impairments.

Driver Response Times to Side Road Path Intrusions from SHRP-2 Naturalistic Database

2020 ◽  
Vol 64 (1) ◽  
pp. 1525-1529
Author(s):  
Swaroop Dinakar ◽  
Jeffrey Muttart ◽  
Jeffrey Suway ◽  
J.S. Forensics ◽  
Jim Marr ◽  
...  
Keyword(s):  
Emergency Response ◽  
Response Times ◽  
Driver Behavior ◽  
The Other ◽  
Response Behavior ◽  
Driver Response ◽  
Emergency Events ◽  
Behavior Systems

In an age where all major manufacturers are trying to get a better understanding of when an emergency response should be triggered, it becomes imperative to learn how humans respond to emergency events. If one can understand driver behavior, systems can be designed around the user to either assist drivers where they fail to perform well or completely eliminate them from the accident avoidance maneuver. In this study, 169 crash and near crash events from the SHRP2 dataset were analyzed. The response behavior of drivers was measured in events where the through drivers’ path was intruded upon by another vehicle perpendicular to its path. Overall, drivers responded significantly faster when the other vehicle failed to stop, and at intersection locations.

First encounter effects in testing of highly automated vehicles during two experimental occasions – The need for recurrent testing

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jonas Andersson ◽  
Azra Habibovic ◽  
Daban Rizgary
Keyword(s):  
Driver Behavior ◽  
Added Value ◽  
Future Research ◽  
Gaze Behavior ◽  
Repeated Testing ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Perceived Safety ◽  
Repeated Interaction ◽  
Over Time

Abstract To explore driver behavior in highly automated vehicles (HAVs), independent researchers are mainly conducting short experiments. This limits the ability to explore drivers’ behavioral changes over time, which is crucial when research has the intention to reveal human behavior beyond the first-time use. The current paper shows the methodological importance of repeated testing in experience and behavior related studies of HAVs. The study combined quantitative and qualitative data to capture effects of repeated interaction between drivers and HAVs. Each driver ( n = 8 n=8 ) participated in the experiment on two different occasions (∼90 minutes) with one-week interval. On both occasions, the drivers traveled approximately 40 km on a rural road at AstaZero proving grounds in Sweden and encountered various traffic situations. The participants could use automated driving (SAE level 4) or choose to drive manually. Examples of data collected include gaze behavior, perceived safety, as well as interviews and questionnaires capturing general impressions, trust and acceptance. The analysis shows that habituation effects were attenuated over time. The drivers went from being exhilarated on the first occasion, to a more neutral behavior on the second occasion. Furthermore, there were smaller variations in drivers’ self-assessed perceived safety on the second occasion, and drivers were faster to engage in non-driving related activities and become relaxed (e. g., they spent more time glancing off road and could focus more on non-driving related activities such as reading). These findings suggest that exposing drivers to HAVs on two (or more) successive occasions may provide more informative and realistic insights into driver behavior and experience as compared to only one occasion. Repeating an experiment on several occasions is of course a balance between the cost and added value, and future research should investigate in more detail which studies need to be repeated on several occasions and to what extent.

scholarly journals Driver behavior and the use of automation in real-world driving

2021 ◽  
Vol 158 ◽  
pp. 106217
Author(s):  
Pnina Gershon ◽  
Sean Seaman ◽  
Bruce Mehler ◽  
Bryan Reimer ◽  
Joseph Coughlin
Keyword(s):  
Real World ◽  
Driver Behavior
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