3DOP: Comfort-oriented Motion Planning for Automated Vehicles with Active Suspensions

Yanggu Zheng, Barys Shyrokau, T. Keviczky
{"title":"3DOP: Comfort-oriented Motion Planning for Automated Vehicles with Active Suspensions","authors":"Yanggu Zheng, Barys Shyrokau, T. Keviczky","doi":"10.1109/iv51971.2022.9827152","DOIUrl":null,"url":null,"abstract":"Motion comfort is the basis of many societal benefits promised by automated driving and motion planning is primarily responsible for this. By planning the spatial trajectory and the velocity profile, motion planners can significantly enhance motion comfort, ideally without sacrificing time efficiency. Active suspensions can push the boundary further by enabling additional degrees of freedom in the controllable vehicle motions. In this paper, we propose to integrate the planning of roll motion into an optimization-based motion planning algorithm called 3DOP(3 Degrees-of-Freedom Optimal Planning), where the conflicting objectives of comfort and time efficiency are optimized. The feasibility of the planned motion is verified in a realistic simulation environment, where feedforward-proportional control suffices to track the speed, path, and roll references. The proposed scheme achieves a significant reduction of motion discomfort, namely by up to 28.1% over the variant without controllable roll motion, or up to 34.2% over an acceleration-bounded driver model. The results suggest considerable potential for improving motion comfort by equipping automated vehicles with active suspensions.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Intelligent Vehicles Symposium (IV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iv51971.2022.9827152","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

Motion comfort is the basis of many societal benefits promised by automated driving and motion planning is primarily responsible for this. By planning the spatial trajectory and the velocity profile, motion planners can significantly enhance motion comfort, ideally without sacrificing time efficiency. Active suspensions can push the boundary further by enabling additional degrees of freedom in the controllable vehicle motions. In this paper, we propose to integrate the planning of roll motion into an optimization-based motion planning algorithm called 3DOP(3 Degrees-of-Freedom Optimal Planning), where the conflicting objectives of comfort and time efficiency are optimized. The feasibility of the planned motion is verified in a realistic simulation environment, where feedforward-proportional control suffices to track the speed, path, and roll references. The proposed scheme achieves a significant reduction of motion discomfort, namely by up to 28.1% over the variant without controllable roll motion, or up to 34.2% over an acceleration-bounded driver model. The results suggest considerable potential for improving motion comfort by equipping automated vehicles with active suspensions.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
3DOP:主动悬架自动驾驶汽车的舒适运动规划
运动舒适是自动驾驶承诺的许多社会效益的基础,而运动规划是主要原因。通过规划空间轨迹和速度轮廓,运动规划者可以显著提高运动舒适性,理想情况下不牺牲时间效率。主动悬架可以通过在可控制的车辆运动中提供额外的自由度来进一步推动边界。在本文中,我们提出将滚动运动的规划整合到一个基于优化的运动规划算法中,称为3DOP(3自由度最优规划),其中优化了舒适性和时间效率的冲突目标。在现实的仿真环境中验证了计划运动的可行性,其中前馈比例控制足以跟踪速度,路径和滚动参考。所提出的方案显著降低了运动不适感,即比无可控侧滚运动的版本减少了28.1%,比有加速度限制的驾驶员模型减少了34.2%。研究结果表明,为自动驾驶汽车配备主动悬架,在改善运动舒适性方面具有相当大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Dynamic Conflict Mitigation for Cooperative Driving Control of Intelligent Vehicles Detecting vehicles in the dark in urban environments - A human benchmark A Sequential Decision-theoretic Method for Detecting Mobile Robots Localization Failures Scene Spatio-Temporal Graph Convolutional Network for Pedestrian Intention Estimation What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1