GA-based Velocity Planning Using Jerk as the Encoding Method for Autonomous Vehicles

被引：0

作者：

Hou, Jing ^{[1
]}

Yu, Junwei ^{[1
]}

Qu, Sanqing ^{[1
]}

Wang, Fa ^{[1
]}

Zi, Yang ^{[1
]}

Chen, Guang ^{[1
]}

机构：

[1] Tongji Univ, Sch Automot Studies, Shanghai, Peoples R China

来源：

2019 3RD CONFERENCE ON VEHICLE CONTROL AND INTELLIGENCE (CVCI) | 2019年

关键词：

autonomous vehicles; velocity planning; genetic algorithm; gene coding method; fitness function;

D O I：

10.1109/cvci47823.2019.8951716

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A technique for the optimal velocity planning using the genetic algorithm for autonomous vehicles is proposed in this paper. A distance-time graph where the dynamic obstacles occupy the corresponding space will be established. Through gene coding method using jerk of genetic algorithm, the feasible distance-time curve is obtained. A fitness function comprehensively evaluates the safety, smoothness, economy, and speed performance of the curve. Through the reproduction and natural selection of several generations, the best individual is selected as the final velocity curve result. The simulation results based on PreScan show that the final velocity curve has good performance. This paper provides an intelligent, convenient and reliable solution for autonomous vehicle velocity planning.

引用

页码：396 / 401

页数：6

共 16 条

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