Zhanyu Wang, Yuqiang Liu, Hongyang Su, Benhong Zhang
{"title":"Research on Collision Avoidance Control in the Same Direction for Intelligent Vehicles Under Emergency Conditions","authors":"Zhanyu Wang, Yuqiang Liu, Hongyang Su, Benhong Zhang","doi":"10.1002/adc2.70012","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>In order to reduce the possibility of collisions during the driving process of intelligent vehicles in the same direction, this paper studies the collision avoidance control of intelligent vehicles in the same direction and designs an active collision avoidance controller. The longitudinal safe distance model, lateral lane change path planning model, and adaptive multi-point preview model of preview distance are established. The longitudinal speed control is carried out by the expert PID control method based on mode switching, the lateral path tracking control is carried out by the sliding mode control method with exponential convergence law, and the active collision avoidance controller is designed in combination with the multi-point preview module that is adaptive to the preview distance. The active collision avoidance controller was jointly simulated using Carsim, Prescan, and Simulink software for emergency lane change scenarios and slow vehicle driving in front. In the emergency lane change scenario, the minimum distance between the two vehicles is 1.9 m, and the path tracking deviation is 0.17 m. In the front vehicle slow driving scenario, the minimum distance between the two vehicles is 2.2 m, and the path tracking deviation is 0.13 m. The controller can realize collision avoidance in two scenarios of 80 and 108 km/h respectively, which shows that the controller is robust and considers the tracking accuracy and steering stability at the same time, which is of reference significance for improving the safety of intelligent vehicles driving in the same direction.</p>\n </div>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"7 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.70012","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Control for Applications","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/adc2.70012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In order to reduce the possibility of collisions during the driving process of intelligent vehicles in the same direction, this paper studies the collision avoidance control of intelligent vehicles in the same direction and designs an active collision avoidance controller. The longitudinal safe distance model, lateral lane change path planning model, and adaptive multi-point preview model of preview distance are established. The longitudinal speed control is carried out by the expert PID control method based on mode switching, the lateral path tracking control is carried out by the sliding mode control method with exponential convergence law, and the active collision avoidance controller is designed in combination with the multi-point preview module that is adaptive to the preview distance. The active collision avoidance controller was jointly simulated using Carsim, Prescan, and Simulink software for emergency lane change scenarios and slow vehicle driving in front. In the emergency lane change scenario, the minimum distance between the two vehicles is 1.9 m, and the path tracking deviation is 0.17 m. In the front vehicle slow driving scenario, the minimum distance between the two vehicles is 2.2 m, and the path tracking deviation is 0.13 m. The controller can realize collision avoidance in two scenarios of 80 and 108 km/h respectively, which shows that the controller is robust and considers the tracking accuracy and steering stability at the same time, which is of reference significance for improving the safety of intelligent vehicles driving in the same direction.