Evolving testing scenario generation and intelligence evaluation for automated vehicles

计算机科学 系统工程 人工智能 工程类
作者
Yining Ma,Wei Jiang,Lingtong Zhang,Junyi Chen,Hong Wang,Chen Lv,Xuesong Wang,Lu Xiong
出处
期刊:Transportation Research Part C-emerging Technologies [Elsevier BV]
卷期号:163: 104620-104620
标识
DOI:10.1016/j.trc.2024.104620
摘要

Interaction between the background vehicles (BVs) and automated vehicles (AVs) in scenario-based testing plays a critical role in evaluating the intelligence of the AVs. Current testing scenarios typically employ predefined or scripted BVs, which inadequately reflect the complexity of human-like social behaviors in real-world driving scenarios, and also lack a systematic metric for evaluating the comprehensive intelligence of AVs. Therefore, this paper proposes an evolving scenario generation method, employing deep reinforcement learning (DRL) to construct human-like BVs that interact with AVs, and this evolving scenario is designed to test and evaluate the intelligence of AVs. Firstly, a class of BV driver models with human-like competitive, mutual, and cooperative driving motivations is designed. Then, utilizing the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and an improved level-k training procedure, the three distinct driver models acquire game-based interactive driving policies. And these driver models are combined to generate evolving scenarios in which they can interact continuously and evolve diverse contents. Next, a framework including safety, driving efficiency, and interaction utility are presented to evaluate and quantify the intelligence performance of 3 systems under test (SUTs), indicating the effectiveness of the evolving scenario for intelligence testing. Finally, the complexity and fidelity of the proposed evolving testing scenario are validated. The results demonstrate that the proposed evolving scenario exhibits the highest level of complexity compared to other baseline scenarios and has more than 85% similarity to naturalistic driving data. This highlights the potential of the proposed method to facilitate the development and evaluation of high-level AVs in a realistic and challenging environment.
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
刚刚
SYLH应助zly采纳,获得30
3秒前
完美世界应助娇气的天亦采纳,获得10
5秒前
7秒前
10秒前
科目三应助彭栋采纳,获得10
12秒前
方文浩发布了新的文献求助10
12秒前
ding应助YWang采纳,获得10
15秒前
15秒前
林宝雯关注了科研通微信公众号
20秒前
23秒前
斯文败类应助GGBOND采纳,获得10
23秒前
星辰大海应助科研通管家采纳,获得10
23秒前
李健的小迷弟应助GGBOND采纳,获得10
23秒前
上官若男应助科研通管家采纳,获得10
23秒前
24秒前
大模型应助科研通管家采纳,获得10
24秒前
圆锥香蕉应助科研通管家采纳,获得20
24秒前
星辰大海应助科研通管家采纳,获得10
24秒前
24秒前
24秒前
Bio应助科研通管家采纳,获得30
24秒前
科研通AI5应助科研通管家采纳,获得10
24秒前
斯文败类应助科研通管家采纳,获得10
24秒前
汉堡包应助科研通管家采纳,获得10
24秒前
25秒前
28秒前
30秒前
30秒前
Dotson发布了新的文献求助10
31秒前
sinsinsin发布了新的文献求助10
32秒前
CodeCraft应助娇气的天亦采纳,获得10
33秒前
34秒前
权思远发布了新的文献求助10
34秒前
彭栋发布了新的文献求助10
34秒前
量子星尘发布了新的文献求助10
35秒前
李爱国应助收集快乐采纳,获得10
36秒前
守墓人完成签到 ,获得积分10
37秒前
38秒前
科研通AI5应助xiaoxiao采纳,获得10
41秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 3989115
求助须知:如何正确求助?哪些是违规求助? 3531367
关于积分的说明 11253688
捐赠科研通 3269986
什么是DOI,文献DOI怎么找? 1804868
邀请新用户注册赠送积分活动 882078
科研通“疑难数据库(出版商)”最低求助积分说明 809105