mlsanityData to 7 Oct 2026

Baidu Apollo EM Motion Planner

arXiv 1807.08048 ↗First posted 20 Jul 2018cs.RO cs.AI cs.LGcited by 99

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Cited by 53 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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 https://github.com/ApolloAuto/apollo/tree/master/modules/planning.

Key facts. Cited by 99 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jul 2018.

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Citing papers per year: 2020: 1; 2021: 10; 2022: 13; 2023: 20; 2024: 25; 2025: 19; 2026: 11.

99 papers.

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  1. 2023GPT-Driver: Learning to Drive with GPTcited by 344
  2. 2024NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarkingcited by 343
  3. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  4. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 238
  5. 2023A Language Agent for Autonomous Drivingcited by 161
  6. 2025End-to-End Driving with Online Trajectory Evaluation via BEV World Modelcited by 145
  7. 2025Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tailcited by 136
  8. 2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradientscited by 122
  9. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
  10. 2022PlanT: Explainable Planning Transformers via Object-Level Representationscited by 105
  11. 2024Towards learning-based planning:The nuPlan benchmark for real-world autonomous drivingcited by 103
  12. 2025Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillationcited by 93
38 more
  1. 2024DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mambacited by 87
  2. 2021SimNet: Learning Reactive Self-driving Simulations from Real-world Observationscited by 77
  3. 2021SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policiescited by 68
  4. 2024Embodied Understanding of Driving Scenarioscited by 63
  5. 2025A Survey of World Models for Autonomous Drivingcited by 60
  6. 2023LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoningcited by 59
  7. 2024Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Followingcited by 41
  8. 2024UnO: Unsupervised Occupancy Fields for Perception and Forecastingcited by 40
  9. 2024Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?cited by 38
  10. 2024Asynchronous Large Language Model Enhanced Planner for Autonomous Drivingcited by 38
  11. 2025Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learningcited by 38
  12. 2025HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decodercited by 35
  13. 2023PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Drivingcited by 33
  14. 2021PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulationcited by 29
  15. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  16. 2022Bootstrap Motion Forecasting With Self-Consistent Constraintscited by 23
  17. 2025Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modelingcited by 22
  18. 2021Deep Structured Reactive Planningcited by 18
  19. 2024DeTra: A Unified Model for Object Detection and Trajectory Forecastingcited by 15
  20. 2022Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinementcited by 14
  21. 2022RAP: Risk-Aware Prediction for Robust Planningcited by 12
  22. 2023CCIL: Context-conditioned imitation learning for urban drivingcited by 12
  23. 2025Decoupled Diffusion Sparks Adaptive Scene Generationcited by 12
  24. 2022Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehiclescited by 11
  25. 2024QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Drivingcited by 11
  26. 2024Instruct Large Language Models to Drive like Humanscited by 11
  27. 2024Planning with Adaptive World Models for Autonomous Drivingcited by 11
  28. 2024Enhancing Online Road Network Perception and Reasoning with Standard Definition Mapscited by 11
  29. 2024ComDrive: Comfort-Oriented End-to-End Autonomous Drivingcited by 11
  30. 2023Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulationcited by 10
  31. 2022THMA: Tencent HD Map AI System for Creating HD Map Annotationscited by 9
  32. 2023Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planningcited by 9
  33. 2023Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Drivingcited by 9
  34. 2025Enhancing Autonomous Driving Safety with Collision Scenario Integrationcited by 9
  35. 2020Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectorycited by 8
  36. 2023LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Cloudscited by 7
  37. 2024From Dashcam Videos to Driving Simulations: Stress Testing Automated Vehicles against Rare Eventscited by 7
  38. 2025LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarioscited by 7

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