亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Simulation-based Optimization of Autonomous Driving Behaviors

复制 计算机科学 弹道 领域(数学) 驾驶模拟器 交通模拟 模拟 实时计算 工程类 运输工程 数学 天文 交叉口(航空) 统计 物理 纯数学
作者
Hashmatullah Sadid,Moeid Qurashi,Constantinos Antoniou
标识
DOI:10.1109/itsc55140.2022.9922604
摘要

Microscopic traffic models (MTMs) are widely used for assessing the impacts of autonomous and connected autonomous vehicles (AVs/CAVs). These models use car following (CF) and lane changing models to replicate the AV and CAV driving behaviors. Several studies attempt to replicate the accurate configuration of these behaviors (especially CF behavior) with many state-of-the-art modeling methods. However, they need to define certain parameters either based on assumptions or estimation by trajectory data from the limited field experiment of AVs and CAVs, and the impacts prediction accuracy depends on the definition of these parameters. For human-driven vehicles, these parameters mimic human drivers, whereas, for AVs and CAVs, most of these parameters could be controlled by an agent (AV and CAV). Therefore, it is possible to train AVs and CAVs to behave in a way that could potentially enhance their related impacts, e.g., traffic efficiency, emissions, and safety. Thus, this paper proposes an optimization framework that tends to find sets of optimized driving parameters for AVs and CAVs under different varying scenarios to achieve pre-defined policy targets (e.g., reducing travel time, number of conflicts). The proposed framework comprises an optimization module and a simulation environment. The differential evolution (DE) method is used within the optimization module to find the optimal values of the CF parameters. The simulation environment is a SUMO-based platform where several simulations are run under certain scenario conditions. An experimental setup is designed to apply the proposed framework under different scenarios of mixed traffic and demand situations. The findings of this study reveal that safety could be potentially improved by optimized values of CF model parameters. For each policy, where higher weight is allocated to safety, generated optimized parameters significantly improve safety as well as efficiency.
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
科研通AI2S应助科研通管家采纳,获得30
21秒前
Owen应助科研通管家采纳,获得10
21秒前
科研通AI2S应助科研通管家采纳,获得10
21秒前
27秒前
44秒前
45秒前
搜集达人应助喜欢对你笑采纳,获得10
50秒前
隐形曼青应助科研通管家采纳,获得10
2分钟前
科研通AI2S应助科研通管家采纳,获得10
2分钟前
老石完成签到 ,获得积分10
3分钟前
3分钟前
CipherSage应助科研通管家采纳,获得10
4分钟前
彭于晏应助科研通管家采纳,获得10
4分钟前
科研通AI2S应助科研通管家采纳,获得10
4分钟前
火星上向珊完成签到,获得积分10
4分钟前
4分钟前
wdxx发布了新的文献求助30
4分钟前
liufan完成签到 ,获得积分10
5分钟前
lmplzzp完成签到,获得积分10
5分钟前
橙子味的邱憨憨完成签到 ,获得积分10
5分钟前
杪夏二八完成签到 ,获得积分10
5分钟前
wdxx完成签到,获得积分10
5分钟前
649981108发布了新的文献求助10
6分钟前
6分钟前
649981108完成签到,获得积分10
6分钟前
6分钟前
研友_892kOL完成签到,获得积分10
6分钟前
脑洞疼应助李小猫采纳,获得10
7分钟前
7分钟前
李小猫完成签到,获得积分10
7分钟前
7分钟前
李小猫发布了新的文献求助10
7分钟前
8分钟前
8分钟前
8分钟前
9分钟前
10分钟前
Tiger完成签到,获得积分10
10分钟前
10分钟前
10分钟前
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
Immigrant Incorporation in East Asian Democracies 600
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 3968504
求助须知:如何正确求助?哪些是违规求助? 3513278
关于积分的说明 11167214
捐赠科研通 3248660
什么是DOI,文献DOI怎么找? 1794386
邀请新用户注册赠送积分活动 875030
科研通“疑难数据库(出版商)”最低求助积分说明 804638