A holistic safe planner for automated driving considering interaction with human drivers

Vijayakumar, H., Zhao, D. , Lan, J. , Zhao, W., Tian, D., Li, D., Zhou, Q. and Song, K. (2023) A holistic safe planner for automated driving considering interaction with human drivers. IEEE Transactions on Intelligent Vehicles, 9(1), pp. 2061-2076. (doi: 10.1109/TIV.2023.3317338)

Abstract

This article advances state-of-the-art automated driving systems with a comprehensive framework that encompasses decision making, maneuver planning, and trajectory tracking considering safety, computational efficiency, and passenger comfort. In face of the co-existence of automated vehicles (AVs) and human-driven vehicles (HDVs), a decision making framework of AVs is proposed for safe lane keeping or changing. The decision making is based on the HDVs' future motion predicted by a learning-based Long Short-Term Memory model. To quantify the uncertainties in prediction, an error ellipse is used to capture the model deviations from the ground truth to ensure driving safety. This article develops a novel method that leverages lower-order parametric curves to efficiently generate feasible, safe, and comfortable lateral movements for AVs. The planner is complemented by maneuver replanning that can guide the AV back to the original lane when confronted with unexpected blockages from surrounding vehicles. Based on real-world datasets, simulation results show that the proposed method achieves curvature compatibility, shorter trajectory length in lateral maneuvers, accurate trajectory tracking, and effective collision avoidance in lane changing.

• Item Type: Articles
• Additional Information: This work was supported in part by the EPSRC Innovation Fellowship of the Engineering and Physical Sciences Research Council of U.K. under Grant EP/S001956/2, in part by the Royal Society-Newton Advanced Fellowship under Grant NAF\R1\201213, and in part by the State Key Laboratory of Engines at Tianjin University under Grant K2022-13. Jianglin Lan was supported by a Leverhulme Trust Early Career Fellowship under Award ECF2021-517.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Dezong and Vijayakumar, Hari and Lan, Dr Jianglin
• Authors: Vijayakumar, H., Zhao, D., Lan, J., Zhao, W., Tian, D., Li, D., Zhou, Q., and Song, K.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Intelligent Vehicles
• Publisher: IEEE
• ISSN: 2379-8858
• ISSN (Online): 2379-8904
• Copyright Holders: Copyright © 2024, IEEE
• First Published: First published in IEEE Transactions on Intelligent Vehicles 9(1):2061-2076
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
314774		Toward Energy Efficient Autonomous Vehiciles via Cloud-Aided learning	Dezong Zhao	Engineering and Physical Sciences Research Council (EPSRC)	EP/S001956/2	ENG - Autonomous Systems & Connectivity