Emergency Collision Avoidance Decision-making for Autonomous Vehicles: A Model-based Reinforcement Learning Approach

强化学习 避碰 计算机科学 人工智能 碰撞 模拟 计算机安全
作者
Xiangkun He,Chen Lv,Xuewu Ji,Yahui Liu
标识
DOI:10.1109/cvci56766.2022.9964555
摘要

The challenging task of "intelligent vehicles" opens up a new frontier to enhancing traffic safety. However, how to determine driving behavior timely and effectively is one of the most crucial concerns, which directly affects vehicle's collision avoidance capability and dynamics stability, particularly in emergency scenarios. Here, this paper presents a novel model-based reinforcement learning (RL) solution for driving behavior decision-making of autonomous vehicles in emergency situations. Firstly, in order to generate initial training data, a rule-based expert system (ES) is designed by analyzing human drivers' emergency collision avoidance manipulations and tire dynamics characteristics. Secondly, an imitative learning (IL) algorithm is developed to clone ES's driving behavior through softmax classifier and mini-batch stochastic gradient descent (MSGD) method. Thirdly, A model-prediction-based Q(λ)-learning with function approximation is presented to determine driving policy online, which integrates vehicle system model and neural network model from IL. Finally, the results of both simulation and experiment show that our approach can effectively coordinate multiple motion control systems to improve collision avoidance capability and vehicle dynamics stability at or close to the driving limits.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
青莲完成签到,获得积分20
1秒前
故意的成协完成签到 ,获得积分20
1秒前
Cora完成签到,获得积分10
1秒前
GHGH666发布了新的文献求助10
1秒前
黑妹完成签到 ,获得积分10
1秒前
友好的冥王星完成签到,获得积分10
3秒前
sht应助羊铁身采纳,获得10
3秒前
苏我入鹿完成签到,获得积分10
3秒前
3秒前
小卡比发布了新的文献求助10
3秒前
SYLH应助jiejie采纳,获得30
3秒前
现实的日记本完成签到,获得积分10
4秒前
4秒前
4秒前
清秀白梦完成签到 ,获得积分10
4秒前
holly完成签到,获得积分10
4秒前
蒲团了道真完成签到,获得积分10
4秒前
愉快迎南完成签到,获得积分10
4秒前
清爽白开水完成签到 ,获得积分10
4秒前
5秒前
与月同行发布了新的文献求助10
5秒前
Ava应助我爱学习采纳,获得10
5秒前
yk完成签到 ,获得积分10
5秒前
5秒前
谷歌完成签到,获得积分10
6秒前
顺利的语山完成签到,获得积分20
6秒前
871624521完成签到,获得积分10
6秒前
隐形曼青应助苗苗043采纳,获得10
6秒前
Cmax_关注了科研通微信公众号
6秒前
chen完成签到,获得积分10
7秒前
7秒前
务实的紫伊完成签到,获得积分10
7秒前
zwy1216完成签到,获得积分10
7秒前
didi完成签到,获得积分10
8秒前
zzy加油发布了新的文献求助10
8秒前
李保龙完成签到 ,获得积分10
8秒前
Crazykk完成签到,获得积分10
9秒前
guoguo发布了新的文献求助30
9秒前
9秒前
顾矜应助白茶采纳,获得10
9秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969033
求助须知:如何正确求助?哪些是违规求助? 3513900
关于积分的说明 11170818
捐赠科研通 3249256
什么是DOI,文献DOI怎么找? 1794708
邀请新用户注册赠送积分活动 875326
科研通“疑难数据库(出版商)”最低求助积分说明 804759