A Causal Inference–Based Speed Control Framework for Discretionary Lane-Changing Processes
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 8
Abstract
Vehicle lane changing is one of the most important topics in autonomous driving and is the behavior most likely to cause vehicle accidents. In order to optimize the performance of vehicle lane change by controlling the speed of the vehicle, which makes the lane change safer, more efficient, and more eco-friendly, a causal inference-based speed control framework is proposed in this study. First, variables affecting the lane change efficiency are selected and their treatment effects are obtained by conducting causal effect analysis and causal refutation analysis. Second, the conditional average treatment effect is estimated by taking the safety and eco-friendly requirements into consideration and applying the double machine learning version causal forest model. Finally, a controller is built, and grid search is used to optimize lane change decision-making. By conducting numerical experiments, the average lane change duration decreased by 5.9% and average velocity increased from to . The results show that the framework has good performance on improving the efficiency and environmental friendliness of a lane change process while ensuring safety. The proposed framework provides a new approach to speed control for discretionary lane-changing behavior. The approach can be well adapted to a vehicle-to-vehicle communication environment and is expected to be applied to vehicle-assisted driving systems in the future.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors confirm contribution to the paper as follows: study conception and design: Yi Zhao and Zhen Zhou; data collection: Zhen Zhou, Minghao Li, and Yuyang Bao; analysis and interpretation of results: Yi Zhao and Zhen Zhou; and draft manuscript preparation: Zhen Zhou, Minghao Li, and Yuyang Bao. All authors reviewed the results and approved the final version of the manuscript.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 8 • August 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Nov 15, 2022
Accepted: Mar 13, 2023
Published online: May 27, 2023
Published in print: Aug 1, 2023
Discussion open until: Oct 27, 2023
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