文献类型：期刊文献

英文题名：Priority Sequential Inference: Improving Safety for Efficient Autonomous Highway Driving Using MARL

作者：Yang, Tao[1];Shi, Xinhao[1];Yang, Yulin[2];Xu, Cheng[1];Liu, Hongzhe[1];Zeng, Qinghan[2]

通讯作者：Xu, C[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Sci & Tech Ctr Innovat, Unit PLA 32178, Beijing 100012, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

年份：2025

卷号：25

期号：6

起止页码：10390-10401

外文期刊名：IEEE SENSORS JOURNAL

收录：;EI(收录号：20250517788567);Scopus(收录号：2-s2.0-85216362055);WOS:【SCI-EXPANDED(收录号:WOS:001445067100016)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62171042, Grant 62102033, and Grant U24A20331; in part by the Research and Development Program of Beijing Municipal Education Commission under Grant KZ202211417048; in part by the Project of Construction and Support for High-Level Innovative Teams of Beijing Municipal Institutions under Grant BPHR20220121; in part by Beijing Natural Science Foundation under Grant 4232026 and Grant 4242020; and in part by the Academic Research Projects of Beijing Union University under Grant ZKZD202302 and Grant ZK20202403.

语种：英文

外文关键词：Safety; Decision making; Merging; Autonomous vehicles; Vehicle dynamics; Predictive models; Navigation; Inference algorithms; Cognition; Accuracy; Autonomous driving; future reasoning; highway safety; mixed traffic; multiagent reinforcement learning (MARL); risk avoidance; sequential decision-making

摘要：Recent advancements in multiagent reinforcement learning (MARL) have shown significant progress in the field of autonomous driving. However, the key issue of guaranteeing safe decision-making and control of autonomous vehicles (AVs) in complex and dynamic traffic environments, while avoiding collisions, remains a critical challenge. To further enhance the safety and reliability of autonomous driving systems, this article presents a novel MARL approach called priority sequential inference proximal policy optimization (PSIPPO) to enhance the decision-making capabilities and safety of AVs in mixed traffic highway environments. The proposed method introduces a sequential decision-making framework that enables AVs to make informed decisions based on the actions and states of preceding agents, effectively managing the order of decisions and prioritizing critical ones. Moreover, PSIPPO incorporates a future reasoning mechanism, allowing AVs to predict and respond to potential collision risks by simulating road scenarios several time steps ahead. An adaptive risk avoidance strategy (RAS) is also introduced to assess the appropriateness and safety of lane changes, considering factors, such as intervehicle distance, relative velocity, and acceleration. Extensive experiments conducted in various highway driving scenarios demonstrate the superior performance of PSIPPO compared with several mainstream baseline MARL methods, achieving higher rewards, maintaining higher average speeds, and significantly reducing collision rates, especially in complex traffic conditions.