Learning From Naturalistic Driving Data for Human-Like Autonomous Highway Driving

Driving in a human-like manner is important for an autonomous vehicle to be a smart and predictable traffic participant. To achieve this goal, parameters of the motion planning module should be carefully tuned, which needs great effort and expert knowledge. In this study, a method of learning cost p...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2021-12, Vol.22 (12), p.7341-7354
Main Authors: Xu, Donghao, Ding, Zhezhang, He, Xu, Zhao, Huijing, Moze, Mathieu, Aioun, Francois, Guillemard, Franck
Format: Article
Language:English
Subjects:
Advanced driver assistance systems
Autonomous vehicles
Cost function
Driving
Human motion
Lane changing
Learning
Motion planning
Parameters
Planning
Robots
Traffic planning
Trajectories
Trajectory
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Summary:Driving in a human-like manner is important for an autonomous vehicle to be a smart and predictable traffic participant. To achieve this goal, parameters of the motion planning module should be carefully tuned, which needs great effort and expert knowledge. In this study, a method of learning cost parameters of a motion planner from naturalistic driving data is proposed. The learning is achieved by encouraging the selected trajectory to approximate the human driving trajectory under the same traffic situation. The employed motion planner follows a widely accepted methodology that first samples candidate trajectories in the trajectory space, then select the one with minimal cost as the planned trajectory. Moreover, in addition to traditional factors such as comfort, efficiency and safety, the cost function is proposed to incorporate incentive of behavior decision like a human driver, so that both lane change decision and motion planning are coupled into one framework. Two types of lane incentive cost - heuristic and learning based - are proposed and implemented. To verify the validity of the proposed method, a data set is developed by using the naturalistic trajectory data of human drivers collected on the motorways in Beijing, containing samples of lane changes to the left and right lanes, and car followings. Experiments are conducted with respect to both lane change decision and motion planning, and promising results are achieved.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2020.3001131