基于人类驾驶员在跟车高风险情景中的经验轨迹数据优化智能网联车辆纵向控制模型参数
Optimizing longitudinal control model parameters of connected and automated vehicles using empirical trajectory data of human drivers in risky car-following scenarios
邢璐 1伍丹 2唐幼仪 3李烨4
作者信息
- 1. School of Traffic and Transportation Engineering,Changsha University of Science and Technology,Changsha 410114,China;Department of Automation,Beijing National Research Center for Information Science and Technology,Tsinghua University,Beijing 100084,China
- 2. School of Traffic and Transportation Engineering,Central South University,Changsha 410075,China
- 3. School of Traffic and Transportation Engineering,Changsha University of Science and Technology,Changsha 410114,China
- 4. School of Traffic and Transportation Engineering,Central South University,Changsha 410075,China;Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems,Changsha University of Science and Technology,Changsha 410114,China
- 折叠
摘要
智能网联车辆(CAV)具有提高驾驶安全性的巨大潜力,CAV的基本性能评估准则之一是其能否在真实的交通情景中比人类驾驶员更安全地行驶.本研究提出了一种基于人类驾驶员在跟车高风险情景中的经验轨迹数据来优化CAV纵向控制模型参数的方法.首先,从经验轨迹数据中提取初始跟车车组(I-CFP).然后,基于模型默认参数值,采用自适应巡航控制模型(ACC)和协同自适应巡航控制模型(CACC)进行仿真,生成模拟跟车车组(S-CFP)的原始轨迹.最后,应用遗传算法来优化ACC和CACC模型的控制参数,并生成优化后的跟车车组(O-CFP)轨迹.结果表明,S-CFP的安全性优于I-CFP,而O-CFP具有最佳的安全性能.ACC/CACC模型中的优化参数多样且与默认参数不同,表明最佳模型参数会随跟车情景不同而变化,进而为减少追尾碰撞风险提供了有价值的视角.
Abstract
Connected and automated vehicles(CAVs)have great potential to improve driving safety.A basic performance evaluation criterion of CAVs is whether they can drive more safely than human drivers in real traffic scenarios.This study proposes a method to optimize longitudinal control model parameters of CAVs using empirical trajectory data of human drivers in risky car-following scenarios.Firstly,the initial car-following pairs(I-CFP)are extracted from empirical trajectory data.Then,two types of real longitudinal control models of CAVs,the adaptive cruise control(ACC)and the cooperative ACC(CACC)control models,are employed for simulation in the car-following scenarios with default parameter values,which generate original trajectories of simulated car-following pairs(S-CFP).Finally,a genetic algorithm(GA)is applied to optimize control model parameters of ACC and CACC vehicles and generate optimized trajectories of car-following pairs(O-CFP).Results indicate that safety condition of S-CFP is better than that of I-CFP,while the O-CFP has the best safety performance.The optimized parameters in the ACC/CACC models are diverse and different from the default parameters,indicating that the best model parameters vary with different car-following scenarios.Findings of this study provide a valuable perspective to reduce the rear-end collision risks.
关键词
交通安全/智能网联车辆/自适应巡航控制/协同自适应巡航控制/纵向控制模型参数Key words
traffic safety/connected and automated vehicle/adaptive cruise control/cooperative adaptive cruise control/longitudinal control model parameters引用本文复制引用
基金项目
National Natural Science Foundation of China(52102405)
National Natural Science Foundation of China(71901223)
Natural Science Foundation of Hunan Province,China(2021JJ40746)
Natural Science Foundation of Hunan Province,China(2021JJ40603)
Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems(Changsha University of Sci(kfj220701)
Scientific Research Program of the Education Department of Hunan Province,China(21B0335)
China Postdoctoral Science Foundation(2023M731962)
出版年
2023
NETL
NSTL
维普
万方数据