Branimir Škugor;Jakov Topić;Joško Deur;Vladimir Ivanovic;H. Eric Tseng
{"title":"自动驾驶汽车接近有行人的无信号人行横道的交互感知最优安全速度控制","authors":"Branimir Škugor;Jakov Topić;Joško Deur;Vladimir Ivanovic;H. Eric Tseng","doi":"10.1109/TCST.2025.3561056","DOIUrl":null,"url":null,"abstract":"The proposed autonomous vehicle (AV) safe speed strategy is based on a scenario- and grid-based stochastic model predictive control (SMPC) and a probabilistic neural network (NN) model aimed to predict pedestrian behavior when approaching unsignalized crosswalk. The SMPC problem is formulated to minimize the vehicle traveling time, while accounting for vehicle-pedestrian interaction and keeping the risk of collision with pedestrian low. The vehicle control trajectory is conveniently described by only two parameters to be optimized: the vehicle acceleration and the target speed. Apart from reducing the computational complexity, this simplification facilitates the NN prediction model design in terms of lowering the number of model inputs. The proposed SMPC strategy is verified against a baseline control strategy by means of large-scale stochastic simulations. The verification results indicate that the SMPC strategy on average results in significantly lower vehicle traveling time and less aggressive decelerations, while avoiding pedestrian collisions.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1864-1878"},"PeriodicalIF":3.9000,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Interaction-Aware Optimal Safe Speed Control for Autonomous Vehicles Approaching Unsignalized Crosswalks With Pedestrians\",\"authors\":\"Branimir Škugor;Jakov Topić;Joško Deur;Vladimir Ivanovic;H. Eric Tseng\",\"doi\":\"10.1109/TCST.2025.3561056\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The proposed autonomous vehicle (AV) safe speed strategy is based on a scenario- and grid-based stochastic model predictive control (SMPC) and a probabilistic neural network (NN) model aimed to predict pedestrian behavior when approaching unsignalized crosswalk. The SMPC problem is formulated to minimize the vehicle traveling time, while accounting for vehicle-pedestrian interaction and keeping the risk of collision with pedestrian low. The vehicle control trajectory is conveniently described by only two parameters to be optimized: the vehicle acceleration and the target speed. Apart from reducing the computational complexity, this simplification facilitates the NN prediction model design in terms of lowering the number of model inputs. The proposed SMPC strategy is verified against a baseline control strategy by means of large-scale stochastic simulations. The verification results indicate that the SMPC strategy on average results in significantly lower vehicle traveling time and less aggressive decelerations, while avoiding pedestrian collisions.\",\"PeriodicalId\":13103,\"journal\":{\"name\":\"IEEE Transactions on Control Systems Technology\",\"volume\":\"33 5\",\"pages\":\"1864-1878\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-03-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Control Systems Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10990170/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Control Systems Technology","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10990170/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Interaction-Aware Optimal Safe Speed Control for Autonomous Vehicles Approaching Unsignalized Crosswalks With Pedestrians
The proposed autonomous vehicle (AV) safe speed strategy is based on a scenario- and grid-based stochastic model predictive control (SMPC) and a probabilistic neural network (NN) model aimed to predict pedestrian behavior when approaching unsignalized crosswalk. The SMPC problem is formulated to minimize the vehicle traveling time, while accounting for vehicle-pedestrian interaction and keeping the risk of collision with pedestrian low. The vehicle control trajectory is conveniently described by only two parameters to be optimized: the vehicle acceleration and the target speed. Apart from reducing the computational complexity, this simplification facilitates the NN prediction model design in terms of lowering the number of model inputs. The proposed SMPC strategy is verified against a baseline control strategy by means of large-scale stochastic simulations. The verification results indicate that the SMPC strategy on average results in significantly lower vehicle traveling time and less aggressive decelerations, while avoiding pedestrian collisions.
期刊介绍:
The IEEE Transactions on Control Systems Technology publishes high quality technical papers on technological advances in control engineering. The word technology is from the Greek technologia. The modern meaning is a scientific method to achieve a practical purpose. Control Systems Technology includes all aspects of control engineering needed to implement practical control systems, from analysis and design, through simulation and hardware. A primary purpose of the IEEE Transactions on Control Systems Technology is to have an archival publication which will bridge the gap between theory and practice. Papers are published in the IEEE Transactions on Control System Technology which disclose significant new knowledge, exploratory developments, or practical applications in all aspects of technology needed to implement control systems, from analysis and design through simulation, and hardware.