Baidu Apollo EM Motion Planner

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.

[1]  Edwin Olson,et al.  Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment , 2015, Autonomous Robots.

[2]  Hongbin Zha,et al.  A real-time motion planner with trajectory optimization for autonomous vehicles , 2012, 2012 IEEE International Conference on Robotics and Automation.

[3]  David Hsu,et al.  Integrated Perception and Planning in the Continuous Space: A POMDP Approach , 2013, Robotics: Science and Systems.

[4]  Sebastian Thrun,et al.  Junior: The Stanford entry in the Urban Challenge , 2008, J. Field Robotics.

[5]  Julius Ziegler,et al.  Optimal trajectory generation for dynamic street scenarios in a Frenét Frame , 2010, 2010 IEEE International Conference on Robotics and Automation.

[6]  Jin-Woo Lee,et al.  Tunable and stable real-time trajectory planning for urban autonomous driving , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[7]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.

[8]  Edwin Olson,et al.  MPDM: Multipolicy decision-making in dynamic, uncertain environments for autonomous driving , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Edwin Olson,et al.  Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment , 2015, Autonomous Robots.

[10]  Rüdiger Dillmann,et al.  Probabilistic decision-making under uncertainty for autonomous driving using continuous POMDPs , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[11]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[12]  Julius Ziegler,et al.  Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Michael Stolz,et al.  Search-Based Optimal Motion Planning for Automated Driving , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[14]  Jin-Woo Lee,et al.  Motion planning for autonomous driving with a conformal spatiotemporal lattice , 2011, 2011 IEEE International Conference on Robotics and Automation.