Baidu Apollo EM Motion Planner
Haoyang Fan, Fan Zhu, Changchun Liu, Liangliang Zhang, Li Zhuang, Dong, Li, Weicheng Zhu, Jiangtao Hu, Hongye Li, Qi Kong

TL;DR
This paper presents a real-time, hierarchical motion planning system for autonomous vehicles based on Baidu Apollo, capable of handling complex urban and highway scenarios with safety and scalability.
Contribution
It introduces a scalable, multi-layer motion planning framework combining dynamic programming and spline-based quadratic programming for autonomous driving.
Findings
Deployed on dozens of vehicles since 2017
Tested over 3,380 hours and 68,000 km in urban scenarios
Achieved safe, smooth, and scalable autonomous navigation
Abstract
In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform. The developed system aims to address the industrial level-4 motion planning problem while considering safety, comfort and scalability. The system covers multilane and single-lane autonomous driving in a hierarchical manner: (1) The 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. The planner is…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Traffic control and management · Robotic Path Planning Algorithms
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
