Chandrayee Basu
Qian Yang
ABSTRACT
With progress in enabling autonomous cars to drive safely on the road, it is time to start asking how they should be driving. A common answer is that they should be adopting their users' driving style. This makes the assumption that users want their autonomous cars to drive like they drive - aggressive drivers want aggressive cars, defensive drivers want defensive cars. In this paper, we put that assumption to the test. We find that users tend to prefer a significantly more defensive driving style than their own. Interestingly, they prefer the style they think is their own, even though their actual driving style tends to be more aggressive. We also find that preferences do depend on the specific driving scenario, opening the door for new ways of learning driving style preference.
- P. Abbeel and A. Y. Ng. Apprenticeship learning via inverse reinforcement learning. In Proceedings of the twenty-first international conference on Machine learning, page 1. ACM, 2004. Google Scholar
Digital Library
- ASHRAE. Standard 55 - thermal environmental conditions for human occupancy, 2010.Google Scholar
- N. Banovic, T. Buzali, F. Chevalier, J. Mankoff, and A. K. Dey. Modeling and understanding human routine behavior. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pages 248--260. ACM, 2016. Google ScholarDigital Library
- M. Brackstone and M. McDonald. Car-following: a historical review. Transportation Research Part F: Traffic Psychology and Behaviour, 2(4):181--196, 1999.Google ScholarCross Ref
- M. Elhenawy, A. Jahangiri, H. A. Rakha, and I. El-Shawarby. Modeling driver stop/run behavior at the onset of a yellow indication considering driver run tendency and roadway surface conditions. Accident Analysis & Prevention, 83:90--100, 2015.Google ScholarCross Ref
- HERE360. How to humanize the autonomous car. http://360.here.com/2015/04/23/humanized-driving/, 2015.Google Scholar
- J.-H. Hong, B. Margines, and A. K. Dey. A smartphone-based sensing platform to model aggressive driving behaviors. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems, pages 4047--4056. ACM, 2014. Google ScholarDigital Library
- M. S. Horswill and F. P. McKenna. The effect of perceived control on risk taking1. Journal of Applied Social Psychology, 29(2):377--391, 1999.Google ScholarCross Ref
- M. Kuderer, S. Gulati, and W. Burgard. Learning driving styles for autonomous vehicles from demonstration. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pages 2641--2646. IEEE, 2015.Google ScholarCross Ref
- C.-P. Lam, A. Y. Yang, and S. S. Sastry. An efficient algorithm for discrete-time hidden mode stochastic hybrid systems. In Control Conference (ECC), 2015 European, pages 1212--1218. IEEE, 2015.Google ScholarCross Ref
- S. E. Lee, E. C. Olsen, and W. W. Wierwille. A comprehensive examination of naturalistic lane-changes. Technical report, Virginia Tech Transportation Institute, 2004.Google Scholar
- H. M. Mandalia and M. D. D. Salvucci. Using support vector machines for lane-change detection. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, volume 49, pages 1965--1969. SAGE Publications, 2005.Google ScholarCross Ref
- OpenDS. Welcome to opends 4.0! https://www.opends.eu/home, 2016.Google Scholar
- D. Sadigh, S. S. Sastry, S. A. Seshia, and A. Dragan. Information gathering actions over human internal state. In Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, pages 66--73. IEEE, 2016.Google ScholarCross Ref
- D. D. Salvucci. Inferring driver intent: A case study in lane-change detection. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, volume 48, pages 2228--2231. SAGE Publications, 2004.Google ScholarCross Ref
- S. Scherer, A. Dettmann, F. Hartwich, T. Pech, A. C. Bullinger, J. F. Krems, and G. Wanielik. How the driver wants to be driven-modelling driving styles in highly automated driving. Tagungsband, 7:2015--26, 2015.Google Scholar
- D. Silver, J. A. Bagnell, and A. Stentz. Learning from demonstration for autonomous navigation in complex unstructured terrain. The International Journal of Robotics Research, 2010. Google ScholarDigital Library
- O. Taubman-Ben-Ari, M. Mikulincer, and O. Gillath. The multidimensional driving style inventory--scale construct and validation. Accident Analysis & Prevention, 36(3):323--332, 2004.Google ScholarCross Ref
- C. Tomasi and R. Manduchi. Bilateral filtering for gray and color images. In Computer Vision, 1998. Sixth International Conference on, pages 839--846. IEEE, 1998. Google ScholarDigital Library
- H. H. van Huysduynen, J. Terken, J.-B. Martens, and B. Eggen. Measuring driving styles: a validation of the multidimensional driving style inventory. In Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pages 257--264. ACM, 2015. Google ScholarDigital Library
- L. Xu, J. Hu, H. Jiang, and W. Meng. Establishing style-oriented driver models by imitating human driving behaviors. IEEE Transactions on Intelligent Transportation Systems, 16(5):2522--2530, 2015.Google ScholarCross Ref
- N. M. Yusof and J. Karjanto. Comfort determination in autonomous driving style. In 3rd Workshop on User Experience of Autonomous Vehicles at AutoUI'15, pages 257--264. ACM, 2015.Google Scholar
- B. D. Ziebart, A. L. Maas, A. K. Dey, and J. A. Bagnell. Navigate like a cabbie: Probabilistic reasoning from observed context-aware behavior. In Proceedings of the 10th international conference on Ubiquitous computing, pages 322--331. ACM, 2008. Google ScholarDigital Library
Index Terms
- Do You Want Your Autonomous Car To Drive Like You?
Recommendations
Dashboard Design for an Autonomous Car
AutomotiveUI '14: Adjunct Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular ApplicationsIn a fully autonomous car - steering, deceleration and acceleration are completely controlled by the intelligence built into it. This leads to a major change in user experience as the driver needs different information and is less involved in the driving ...
Using Multimodal Displays to Signify Critical Handovers of Control to Distracted Autonomous Car Drivers
Until full autonomy is achieved in cars, drivers will still be expected to take over control of driving, and critical warnings will be essential. This paper presents a comparison of abstract versus language-based multimodal warnings signifying handovers ...
Eyes on a Car: an Interface Design for Communication between an Autonomous Car and a Pedestrian
AutomotiveUI '17: Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular ApplicationsSelf-driving technologies have been increasingly developed and tested in recent years (e.g., Volvo's and Google's self-driving cars). However, only a limited number of investigations have so far been conducted into communication between self-driving ...
Comments