Baidu Apollo EM Motion Planner

By Haoyang Fan, Fan Zhu, Changchun Liu, Liangliang Zhang, L. Zhuang, Dong Li, Jiangtao Hu, Hongye Li, Qi Kong. 2018

393 citations recorded by the source corpus.

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 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.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking (2024, neutral): “Second, we draw inspiration from the literature of rule-based planning for AVs [41, 18, 39, 16] to identify a set of diverse, efficient, and principled metrics that cover multiple facets of the autonomous driving task.”
Parting with Misconceptions about Learning-based Vehicle Motion Planning (2023, background): “Rule-based planners offer a structured, interpretable decision-making framework [17, 21, 22, 23, 24, 25, 26, 27, 28].”
End-To-End Interpretable Neural Motion Planner (2019, methodology): “Optimization Based Planners: Motion planning has long been treated as an independent task that uses the outputs of perception and prediction modules to formulate an optimization problem, usually by manually engineering a cost function [4, 10, 20, 36].”
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives (2023, background): “[30] identify the state grids before generating connective primitives.”
GPT-Driver: Learning to Drive with GPT (2023, methodology): “The rule-based methods Treiber et al. (2000); Thrun et al. (2006); Bacha et al. (2008); Leonard et al. (2008); Urmson et al. (2008); Chen et al. (2015); Sauer et al. (2018); Fan et al. (2018) designed explicit rules to determine driving trajectories.”