Overtaking maneuvers by non linear time scaling over reduced set of learned motion primitives

Vishakh Duggal, K. Bipin, Ashutosh Kumar Singh, Bharath Gopalakrishnan, B. Bharti, A. Khiat, K. Krishna
{"title":"Overtaking maneuvers by non linear time scaling over reduced set of learned motion primitives","authors":"Vishakh Duggal, K. Bipin, Ashutosh Kumar Singh, Bharath Gopalakrishnan, B. Bharti, A. Khiat, K. Krishna","doi":"10.1109/IVS.2015.7225672","DOIUrl":null,"url":null,"abstract":"Overtaking of a vehicle moving on structured roads is one of the most frequent driving behavior. In this work, we have described a Real Time Control System based framework for overtaking maneuver of autonomous vehicles. Proposed framework incorporates Intelligent Planning and Modular control modules. Intelligent Planning module of the framework enables the vehicle to intelligently select the most appropriate behavioral characteristics given the perceived operating environment. Subsequently, Modular control module reduces the search space of overtaking trajectories through an SVM based learning approach. These trajectories are then examined for possible future time collision using Velocity Obstacle. It employs non linear time scaling that provides for continuous trajectories in the space of linear and angular velocities to achieve continuous curvature overtaking maneuvers respecting velocity and acceleration bounds. Further time scaling also can scale velocities to avoid collisions and can compute a time optimal trajectory for the learned behavior. The preliminary results show the appropriateness of our proposed framework in virtual urban environment.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Intelligent Vehicles Symposium (IV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2015.7225672","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Overtaking of a vehicle moving on structured roads is one of the most frequent driving behavior. In this work, we have described a Real Time Control System based framework for overtaking maneuver of autonomous vehicles. Proposed framework incorporates Intelligent Planning and Modular control modules. Intelligent Planning module of the framework enables the vehicle to intelligently select the most appropriate behavioral characteristics given the perceived operating environment. Subsequently, Modular control module reduces the search space of overtaking trajectories through an SVM based learning approach. These trajectories are then examined for possible future time collision using Velocity Obstacle. It employs non linear time scaling that provides for continuous trajectories in the space of linear and angular velocities to achieve continuous curvature overtaking maneuvers respecting velocity and acceleration bounds. Further time scaling also can scale velocities to avoid collisions and can compute a time optimal trajectory for the learned behavior. The preliminary results show the appropriateness of our proposed framework in virtual urban environment.
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
基于非线性时间尺度的超车机动简化学习运动原语集
在结构道路上超车是最常见的驾驶行为之一。在这项工作中,我们描述了一个基于实时控制系统的自动驾驶车辆超车机动框架。该框架结合了智能规划和模块化控制模块。框架的智能规划模块使车辆能够根据感知到的操作环境,智能地选择最合适的行为特征。随后,模块化控制模块通过基于支持向量机的学习方法减小超车轨迹的搜索空间。然后使用速度障碍检查这些轨迹是否可能在未来发生时间碰撞。它采用非线性时间尺度,在线速度和角速度空间中提供连续轨迹,以实现尊重速度和加速度界限的连续曲率超车机动。进一步的时间缩放还可以缩放速度以避免碰撞,并可以为学习行为计算时间最优轨迹。初步结果表明该框架在虚拟城市环境中的适用性。
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
Optimal parameter selection of a Model Predictive Control algorithm for energy efficient driving of heavy duty vehicles Map free lane following based on low-cost laser scanner for near future autonomous service vehicle Real-time small obstacle detection on highways using compressive RBM road reconstruction Developing a framework of Eco-Approach and Departure application for actuated signal control Face orientation estimation for driver monitoring with a single depth camera
×
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