自动化学报2018，Vol.44Issue(1)：35-43,9.DOI:10.16383/j.aas.2018.c160457
一种基于分类回归树的无人车汇流决策方法
Classification and Regression Tree Based Traffic Merging for Method Self-driving Vehicles
摘要
Abstract
Decision-making and planning are important technologies of unmanned vehicle. Logical rule and optimization algorithm are commonly applied to passive merging strategy for road structure change or obstacles. A traffic merging strategy aiming to improve throughput is proposed in this paper. According to different traffic parameters, a large number of typical traffic merging scenarios are selected. For vehicles in different scenarios,decision sequences are encoded and optimal merging decision is obtained by genetic algorithm based on remainder stochastic sampling with replacement (RSSR).Those optimal decisions are used to train classification and regression tree(CART).Specifically,the environmental feature is described by vehicle state and relationship between other vehicles around. Then the relationship between environmental features and decision is modeled by classification and regression tree. Compared with the previous merging strategy it is shown by simulation that the merging strategy based on CART can effectively mitigate disturbance on traffic flow, brought by merging maneuver, and maintain a high through efficiency even in large flow circumstances. Moreover,this method is also rather robust to environmental perception errors,such as positioning error which may exist in implementation.关键词
汇流决策/遗传算法/分类回归树/交通流仿真Key words
Merging strategy/genetic algorithm/classification and regression tree(CART)/simulation of urban mobility (SUMO)引用本文复制引用
苏锑,杨明,王春香,唐卫,王冰..一种基于分类回归树的无人车汇流决策方法[J].自动化学报,2018,44(1):35-43,9.基金项目
国家自然科学基金(91420101),国家磁约束核聚变能研究专项(2012 GB102002)资助Supported by National Natural Science Foundation of China(91420101)and National Magnetic Confinement Fusion Energy Research Project(2012GB102002) （91420101）
