Tongji University researchers pioneer evolutionary decision-making for safer autonomous driving

Tongji University’s research team, led by Yanjun Huang and Hong Chen, has made significant progress in the field of autonomous driving with their latest research article titled “Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation.” This research, published in the journal Engineering, presents a novel online evolutionary decision-making and motion planning framework that ensures safe and rational driving in real-world environments.

The study addresses the crucial aspects of decision-making and motion planning in autonomous driving, aiming to enhance safety and efficiency. The research team has developed a hybrid data- and model-driven approach, combining deep reinforcement learning (DRL) for decision-making and model predictive control (MPC) for motion planning. This framework enables the autonomous vehicle to make rational driving decisions while adhering to multiple constraints defined by the vehicle’s physical limits.

The research team proposes two principles for safety and rationality in the online evolution of autonomous driving. Based on the above framework, a safe-driving envelope is established, and a rational exploration and exploitation scheme is designed that filters out random and unsafe experiences by masking unsafe actions in order to obtain high-quality training data and realize the safe and rational self-evolution of autonomous driving. Based on a safe online-learning mechanism, the continuous evolution of the system within the capability boundary of the planning layer is realized, along with the maximum utilization of the capabilities of the planning layer.

To validate their framework, the research team conducted experiments using a high-fidelity vehicle model and a MATLAB/Simulink co-simulation environment. The results demonstrate that the proposed online-evolution framework generates safer, more rational, and more efficient driving actions in real-world environments.

The research article concludes with future directions for their work. The team plans to enable the agent to learn the MPC parameters, enhancing the flexibility of decision-making and motion planning. Additionally, they aim to investigate more driving tasks under this framework and conduct real vehicle experiments.

This groundbreaking research by Yanjun Huang and Hong Chen’s research team at Tongji University represents a significant advancement in the field of autonomous driving. Their innovative framework for evolutionary decision-making and motion planning not only ensures safe and rational driving but also contributes to improving traffic efficiency.

The paper “Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation”, authored by Kang Yuan, Yanjun Huang, Shuo Yang, Zewei Zhou, Yulei Wang, Dongpu Cao, Hong Chen. Full text of the open access paper: https://doi.org/10.1016/j.eng.2023.03.018. For more information about the Engineering, follow us on Twitter (https://twitter.com/EngineeringJrnl) & like us on Facebook (https://www.facebook.com/EngineeringPortfolio).

About Engineering:

Engineering (ISSN: 2095-8099 IF:12.8) is an international open-access journal that was launched by the Chinese Academy of Engineering (CAE) in 2015. Its aims are to provide a high-level platform where cutting-edge advancements in engineering R&D, current major research outputs, and key achievements can be disseminated and shared; to report progress in engineering science, discuss hot topics, areas of interest, challenges, and prospects in engineering development, and consider human and environmental well-being and ethics in engineering; to encourage engineering breakthroughs and innovations that are of profound economic and social importance, enabling them to reach advanced international standards and to become a new productive force, and thereby changing the world, benefiting humanity, and creating a new future.