Encoding Human Driving Styles in Motion Planning for Autonomous Vehicles

Conference paper (2021)

Authors

Jesper Karlsson KTH Royal Institute of Technology
Sanne van Waveren KTH Royal Institute of Technology
Christian Pek KTH Royal Institute of Technology
Ilaria Torre KTH Royal Institute of Technology
Iolanda Leite KTH Royal Institute of Technology
Jana Tumova KTH Royal Institute of Technology
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Published Date

2021

Language

English

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Abstract

Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the planner and not tailored to the passenger. We present a new approach to encode human driving styles through the use of signal temporal logic and its robustness metrics. Specifically, we use a penalty structure that can be used in many motion planning frameworks, and calibrate its parameters to model different automated driving styles. We combine this penalty structure with a set of signal temporal logic formula, based on the Responsibility-Sensitive Safety model, to generate trajectories that we expected to correlate with three different driving styles: aggressive, neutral, and defensive. An online study showed that people perceived different parameterizations of the motion planner as unique driving styles, and that most people tend to prefer a more defensive automated driving style, which correlated to their self-reported driving style.