Paper Info
Title
Baidu Apollo EM Motion Planner.
Abstract
In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform. developed system aims to address the industrial level-4 motion planning problem while considering safety, comfort and scalability. system covers multilane and single-lane autonomous driving in a hierarchical manner: (1) top layer of the system is a multilane strategy that handles lane-change scenarios by comparing lane-level trajectories computed in parallel. (2) Inside the lane-level trajectory generator, it iteratively solves path and speed optimization based on a Frenet frame. (3) For path and speed optimization, a combination of dynamic programming and spline-based quadratic programming is proposed to construct a scalable and easy-to-tune framework to handle traffic rules, obstacle decisions and smoothness simultaneously. planner is scalable to both highway and lower-speed city driving scenarios. We also demonstrate the algorithm through scenario illustrations and on-road test results. The system described in this manuscript has been deployed to dozens of Baidu Apollo autonomous driving vehicles since Apollo v1.5 was announced in September 2017. As of May 16th, 2018, the system has been tested under 3,380 hours and approximately 68,000 kilometers (42,253 miles) of closed-loop autonomous driving under various urban scenarios. The algorithm described in this manuscript is available at this https URL.
Year
Venue
Field
2018
arXiv: Robotics
Motion planning,Dynamic programming,Obstacle,Simulation,Planner,Real-time computing,Quadratic programming,Engineering,Smoothness,Trajectory,Scalability
DocType
Volume
Citations 
Journal
abs/1807.08048
5
PageRank 
References 
Authors
0.59
6
10
Name
Order
Citations
PageRank
Haoyang Fan161.07
Fan Zhu262.29
Changchun Liu361.07
Liangliang Zhang4167.01
Li Zhuang551.27
Dong Li66120.32
Weicheng Zhu751.27
Jiangtao Hu872.65
Hongye Li950.59
Qi Kong1062.43