This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Revealing Right-Turn Behavior of Human Drivers as a Model for Autonomous Vehicles
Technical Paper
2021-01-0866
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Event:
SAE WCX Digital Summit
Language:
English
Abstract
Although great progress has been made to improve the safety and performance of autonomous vehicles with the ultimate goal of meeting the public expectation of preventing most accidents, the current fleet of autonomous vehicles being tested continues to demonstrate that we still remain distant from that holy grail. One rationalization for some of these accidents has been that different maneuvers performed by such cars are not human-like (i.e. they do not display certain driving patterns to which human drivers are accustomed to). With that in mind, it would be hard to dispute the need for such vehicles to adapt to and somewhat imitate human driving in order to gradually integrate human-driven traffic in the future. In previously published work, we had examined human driver behavior when approaching and proceeding through stop-sign-controlled intersections using data obtained from a large-scale, on-road eye tracking study conducted in instrumented test vehicles to understand and assess human behavior in a naturalistic driving environment. We provided various metrics based on the vehicle dynamics to quantify human behavior at such intersections and suggested the use of such data for bettering autonomous vehicle performance. Here, we apply a similar approach to the analysis of driver behavior during right-turn maneuvers (specifically at stoplights and uncontrolled intersections). We obtained deceleration rates, stopping/slowing speeds, acceleration rates and angular velocities while participants drove specific routes in Los Angeles. As with our previous findings, we propose that our current data can be incorporated into autonomous systems to allow them to act more human-like (e.g. to avoid being rear-ended by other vehicles during right turns) and to better mimic human driving behaviors to provide smoother rides to passengers (e.g. minimizing abrupt changes in vehicle motion).
Authors
Citation
Tavassoli, A., King, D., Xiouris, C., and Krauss, D., "Revealing Right-Turn Behavior of Human Drivers as a Model for Autonomous Vehicles," SAE Technical Paper 2021-01-0866, 2021, https://doi.org/10.4271/2021-01-0866.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 |
Also In
References
- Yurtsever , E. , Lambert , J. , Carballo , A. , and Takeda , K. A Survey of Autonomous Driving: Common Practices and Emerging Technologies IEEE Access 8 58443 58469 2020
- Krauss , D. Forensic Aspects of Driver Perception and Response Tucson, AZ Lawyers & Judges Publishing Company 2015
- Green , M. , Allen , M.J. , Abrams , B.S. , and Weintraub , L. Forensic Vision with Application to Highway Safety Tucson, AZ Lawyers & Judges Publishing Company 2008
- Dewar , R.E. and Olson , P.L. Human Factors in Traffic Safety Second Tucson, AZ Lawyers & Judges Publishing Company 2002
- Land , M.F. and Tatler , B.W. Looking and Acting: Vision and Action in Natural Behaviour Oxford University Press 2009
- Olson , P.L. and Sivak , M. Perception-Response Time to Unexpected Roadway Hazards Human Factors 28 1 91 96 1986
- Fricke , L.B. Traffic Accident Reconstruction, First Edition, Volume 2 of the Traffic Accident Investigation Manual Northwestern University Center for Public Safety 1990
- Triggs , T.J. and Harris , W.G. 1982
- Bokare , P.S. and Maurya , A.K. Acceleration-Deceleration Behaviour of Various Vehicle Types Transportation Research Procedia 25 4733 4749 2017
- El-Shawarby , I. , Rakha , H. , Inman , V. , and Davis , G. Evaluation of Driver Deceleration Behavior at Signalized Intersections Transportation Research Record: Journal of the Transportation Research Board 2018 29 35 2007
- Najm , W. and Smith , D. Modeling Driver Response to Lead Vehicle Decelerating SAE Technical Paper 2004-01-0171 2004 https://doi.org/10.4271/2004-01-0171
- Kodsi , S. and Muttart , J. Modeling Passenger Vehicle Acceleration Profiles from Naturalistic Observations and Driver Testing at Two-Way-Stop Controlled Intersections SAE International Journal of Passenger Cars-Mechanical Systems 3 2010-01-0062 45 56 2010
- Scanlon , J.M. 2017
- Tavassoli , A. , Cymbalist , N. , Dunning , A. , and Krauss , D. Learning from Human Naturalistic Driving Behavior at Stop Signs for Autonomous Vehicles SAE Technical Paper 2019-01-1021 2019 https://doi.org/10.4271/2019-01-1021
- Liu , C. and Zhang , W. Learning the Driver Acceleration/Deceleration Behavior under High-Speed Environments from Naturalistic Driving Data IEEE Intelligent Transportation Systems Magazine 2020 10.1109/MITS.2020.3014115
- Xu , H. and Wu , J. Use of Naturalistic Driving Study Data to Determine Right-Turn Driver Deceleration Behavior at Signalized Intersections Proceedings of the 97thTransportation Research Board Annual Meeting (No. 18-01877) 2018
- Tavassoli , A. , Perlmutter , S. , Bui , D. , Todd , J. et al. Development of a Robust Database for Measuring Human Gaze Behavior and Performance during Naturalistic Driving SAE Technical Paper 2017-01-1369 2017 https://doi.org/10.4271/2017-01-1369
- Bar-Yehuda , Z.
- Warren , A. , Gattis , J. , Duncan , L. , and Costello , T. Analysis of Deceleration in through Lane Before Right Turn Transportation Research Record 2223 1 113 119 2011
- Sama , K. , Morales , Y. , Liu , H. , Akai , N. et al. Extracting Human-Like Driving Behaviors from Expert Driver Data Using Deep Learning IEEE Transactions on Vehicular Technology 69 9 9315 9329 2020