Study on Control for Prevention of Collision Caused by Failure of Localization for Map-Based Automated Driving Vehicle

Author:

Nishimura Shun1,Omae Manabu1

Affiliation:

1. Graduate School of Media and Governance, Keio University, 5322 Endo, Fujisawa, Kanagawa 252-0882, Japan

Abstract

In demonstration experiments of automated driving vehicles, lane departures and collisions with roadside structures due to poor vehicle positioning and self-localization have been reported. In this study, we propose a promising method to prevent such departures and collisions, and then validate the proposed method by applying it to an actual automated driving vehicle. The proposed method monitors the target steering angles computed by the automated driving control and limits them before commanded the actuator when there is a risk of colliding with obstacles. As the above-mentioned control is lower-level, it can prevent an automated driving vehicle from colliding with obstacles without complicating upper-level controls. Experiments on an actual automated driving vehicle showed that the steering control structure of the proposed method could prevent an automated driving vehicle from colliding with obstacles by limiting its target steering angle. In addition, the method does not impose excessive limits on the steering angle when the automated driving vehicle follows a normal path and no risk of collision exists.

Publisher

Fuji Technology Press Ltd.

Subject

Electrical and Electronic Engineering,General Computer Science

Reference14 articles.

1. S. Tsugawa, “A Technological History of Automated Driving of Automobiles,” IATSS Review, Vol.40, No.2, pp. 6-14, 2015 (in Japanese).

2. N. Suganuma and K. Yoneda, “Utilization of Digital Map for Autonomous Vehicle,” J. of the Robotics Society of Japan, Vol.33, No.10, pp. 760-765, 2015 (in Japanese). https://doi.org/10.7210/jrsj.33.760

3. N. Akai et al., “High-Accurate Localization INS and Using Multilayer LiDAR for Autonomous Cars,” Trans. of Society of Automotive Engineers of Japan, Vol.49, No.3, pp. 675-681, 2018 (in Japanese). https://doi.org/10.11351/jsaeronbun.49.675

4. N. Suganuma and T. Uozumi, “Localization Method for Autonomous Vehicle by Fusing GNSS/INS and Lane Marker Detection,” Trans. of Society of Automotive Engineers of Japan, Vol.42, No.5, pp. 1151-1156, 2011 (in Japanese). https://doi.org/10.11351/jsaeronbun.42.1151

5. K. Maeda, J. Takahashi, and P. Raksincharoensak, “Lane-Marker-Based Map Construction and Map Precision Evaluation Methods Using On-Board Cameras for Autonomous Driving,” J. Robot. Mechatron., Vol.32, No.3, pp. 613-623, 2020. https://doi.org/10.20965/jrm.2020.p0613

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3