Predictive Trajectory Planning for On-Road Autonomous Vehicles Based on a Spatiotemporal Risk Field-Reference-Cited by-同舟云学术

Predictive Trajectory Planning for On-Road Autonomous Vehicles Based on a Spatiotemporal Risk Field

Published:2023-01 Issue:1 Volume:15 Page:400-420
ISSN:1939-1390
Container-title:IEEE Intelligent Transportation Systems Magazine
language:
Short-container-title:IEEE Intell. Transport. Syst. Mag.

Author:

Cao Yue¹^ORCID,ShangGuan Wei²^ORCID,Cai Baigen²^ORCID,Chai Linguo¹,Qiu Weizhi¹^ORCID

Affiliation:

1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China

2. School of Electronic and Information Engineering and State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China

Funder

National Key Research and Development Program of China

Civil Aviation Key Laboratory of China

Beijing Natural Science Foundation-Fengtai Rail Transit Frontier Research Joint Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/5117645/10015073/09833472.pdf?arnumber=9833472

Reference46 articles.

1. Efficient Sampling-Based Motion Planning for On-Road Autonomous Driving

2. A Decoupled Trajectory Planning Framework Based on the Integration of Lattice Searching and Convex Optimization

3. The ParallelEye Dataset: A Large Collection of Virtual Images for Traffic Vision Research

4. Investigation of a Longitudinal and Lateral Lane-Changing Motion Planning Model for Intelligent Vehicles in Dynamical Driving Environments

5. Evidential-Based Approach for Trajectory Planning With Tentacles, for Autonomous Vehicles

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