{"title":"基于非对称风险场和强化学习的自动驾驶汽车变道行为","authors":"Wang-Han Gong, Geng Zhang, Bo-Yu Song","doi":"10.1016/j.physa.2025.131009","DOIUrl":null,"url":null,"abstract":"<div><div>Lane-changing (LC) behavior is a common and safety-risky behavior in traffic system, accurately quantizing the risk during LC process and establishing a reasonable LC model are crucial for autonomous vehicles to complete LC process like human-driving vehicles. So far, the risk in LC process is mainly assumed to be symmetric in existing studies and the different safety risks posed by different types of vehicles are ignored. To explore the safety risks posed by different types of vehicles in real traffic, an asymmetric risk field LC model from the perspective of asymmetric risk is established in this paper. In this model, the asymmetric risk is calculated in view of the vehicle size, and the vehicle size is presented as volume based on natural dataset. Also, the risk threshold that is introduced to depict the LC behavior in line with human-driving characteristics is calibrated by applying reinforcement learning (RL) method and NGSIM dataset. Finally, comparison simulation between the proposed model and the symmetric risk model is carried out and the simulation results illustrate that the longitudinal error (LE), the mixed gap error (MGE), and the model error (ME) of the proposed model with real data is lower than that of the symmetric risk model with real data. It shows that the proposed model is more consistent with the real LC trajectory than the symmetric risk model.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"679 ","pages":"Article 131009"},"PeriodicalIF":3.1000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Human-driving like lane-changing behavior of autonomous vehicles based on asymmetric risk field and reinforcement learning\",\"authors\":\"Wang-Han Gong, Geng Zhang, Bo-Yu Song\",\"doi\":\"10.1016/j.physa.2025.131009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Lane-changing (LC) behavior is a common and safety-risky behavior in traffic system, accurately quantizing the risk during LC process and establishing a reasonable LC model are crucial for autonomous vehicles to complete LC process like human-driving vehicles. So far, the risk in LC process is mainly assumed to be symmetric in existing studies and the different safety risks posed by different types of vehicles are ignored. To explore the safety risks posed by different types of vehicles in real traffic, an asymmetric risk field LC model from the perspective of asymmetric risk is established in this paper. In this model, the asymmetric risk is calculated in view of the vehicle size, and the vehicle size is presented as volume based on natural dataset. Also, the risk threshold that is introduced to depict the LC behavior in line with human-driving characteristics is calibrated by applying reinforcement learning (RL) method and NGSIM dataset. Finally, comparison simulation between the proposed model and the symmetric risk model is carried out and the simulation results illustrate that the longitudinal error (LE), the mixed gap error (MGE), and the model error (ME) of the proposed model with real data is lower than that of the symmetric risk model with real data. It shows that the proposed model is more consistent with the real LC trajectory than the symmetric risk model.</div></div>\",\"PeriodicalId\":20152,\"journal\":{\"name\":\"Physica A: Statistical Mechanics and its Applications\",\"volume\":\"679 \",\"pages\":\"Article 131009\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physica A: Statistical Mechanics and its Applications\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378437125006612\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica A: Statistical Mechanics and its Applications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378437125006612","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
Human-driving like lane-changing behavior of autonomous vehicles based on asymmetric risk field and reinforcement learning
Lane-changing (LC) behavior is a common and safety-risky behavior in traffic system, accurately quantizing the risk during LC process and establishing a reasonable LC model are crucial for autonomous vehicles to complete LC process like human-driving vehicles. So far, the risk in LC process is mainly assumed to be symmetric in existing studies and the different safety risks posed by different types of vehicles are ignored. To explore the safety risks posed by different types of vehicles in real traffic, an asymmetric risk field LC model from the perspective of asymmetric risk is established in this paper. In this model, the asymmetric risk is calculated in view of the vehicle size, and the vehicle size is presented as volume based on natural dataset. Also, the risk threshold that is introduced to depict the LC behavior in line with human-driving characteristics is calibrated by applying reinforcement learning (RL) method and NGSIM dataset. Finally, comparison simulation between the proposed model and the symmetric risk model is carried out and the simulation results illustrate that the longitudinal error (LE), the mixed gap error (MGE), and the model error (ME) of the proposed model with real data is lower than that of the symmetric risk model with real data. It shows that the proposed model is more consistent with the real LC trajectory than the symmetric risk model.
期刊介绍:
Physica A: Statistical Mechanics and its Applications
Recognized by the European Physical Society
Physica A publishes research in the field of statistical mechanics and its applications.
Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents.
Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.