mlsanityData to 7 Oct 2026

Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

arXiv 2303.09824 ↗First posted 17 Mar 2023cs.RO cs.AIcited by 112

On the map: End-to-end autonomous driving → End-to-end system architecture

Abstract (arXiv)

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This paper reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey.

Key facts. Cited by 112 papers in the map (data through 7 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 61 of its 183 references that are in the map. First posted on arXiv 17 Mar 2023.

Builds on

61 of 183 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021End-to-end Interpretable Neural Motion Planner17 Jan

Other references · 60

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  3. 2015Continuous control with deep reinforcement learningcited by 3,279
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  5. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  6. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  7. 2016Generative Adversarial Imitation Learningcited by 1,576
  8. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,282
  9. 2016End to End Learning for Self-Driving Carscited by 1,000
  10. 2017Constrained Policy Optimizationcited by 928
  11. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 532
  12. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 517
38 more
  1. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  2. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 281
  3. 2018Learning to Drive in a Daycited by 266
  4. 2016A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehiclescited by 251
  5. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 229
  6. 2020A2D2: Audi Autonomous Driving Datasetcited by 189
  7. 2020Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representationscited by 150
  8. 2019Lyapunov-based Safe Policy Optimization for Continuous Controlcited by 140
  9. 2020A Survey of End-to-End Driving: Architectures and Training Methodscited by 123
  10. 2020LGSVL Simulator: A High Fidelity Simulator for Autonomous Drivingcited by 119
  11. 2016DESPOT: Online POMDP Planning with Regularizationcited by 105
  12. 2018Baidu Apollo EM Motion Plannercited by 99
  13. 2021ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learningcited by 98
  14. 2018CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-drivingcited by 96
  15. 2016Query-Efficient Imitation Learning for End-to-End Autonomous Drivingcited by 83
  16. 2018Conditional Affordance Learning for Driving in Urban Environmentscited by 70
  17. 2019Learning Reward Functions by Integrating Human Demonstrations and Preferencescited by 64
  18. 2022Bridging the Domain Gap for Multi-Agent Perceptioncited by 60
  19. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
  20. 2021ROAD: The ROad event Awareness Dataset for Autonomous Drivingcited by 54
  21. 2022Model-Agnostic Multi-Agent Perception Frameworkcited by 51
  22. 2017Optimization-Based Autonomous Racing of 1:43 Scale RC Carscited by 47
  23. 2021Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Drivingcited by 46
  24. 2021SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Drivingcited by 43
  25. 2018Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Informationcited by 41
  26. 2021Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Trafficcited by 38
  27. 2016Watch This: Scalable Cost-Function Learning for Path Planning in Urban Environmentscited by 33
  28. 2016Learning a Driving Simulatorcited by 33
  29. 2019Driving in Dense Traffic with Model-Free Reinforcement Learningcited by 33
  30. 2021Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimizationcited by 33
  31. 2020Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learningcited by 30
  32. 2022Social Interactions for Autonomous Driving: A Review and Perspectivescited by 27
  33. 2023The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Researchcited by 27
  34. 2021Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Drivingcited by 21
  35. 2019Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environmentcited by 20
  36. 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Trafficcited by 20
  37. 2021A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learningcited by 18
  38. 2022Driving in Real Life with Inverse Reinforcement Learningcited by 18

50 most cited of 60 All 60 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 24; 2024: 49; 2025: 24; 2026: 15.

112 papers.

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  1. 2023DriveLM: Driving with Graph Visual Question Answeringcited by 527
  2. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  3. 2023DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenescited by 359
  4. 2024EMMA: End-to-End Multimodal Model for Autonomous Drivingcited by 226
  5. 2024A Survey on 3D Gaussian Splattingcited by 221
  6. 2025OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Modelcited by 172
  7. 2023Vision Language Models in Autonomous Driving: A Survey and Outlookcited by 124
  8. 2024OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Drivingcited by 93
  9. 2024PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Drivingcited by 84
  10. 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
  11. 2024A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlookcited by 65
  12. 2023How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligencecited by 55
38 more
  1. 2024V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Modelscited by 47
  2. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 39
  3. 2025Simultaneous Multi-Robot Motion Planning with Projected Diffusion Modelscited by 37
  4. 2025Planning with Reasoning using Vision Language World Modelcited by 36
  5. 2023BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detectioncited by 35
  6. 2024Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Modelscited by 32
  7. 2024WcDT: World-centric Diffusion Transformer for Traffic Scene Generationcited by 31
  8. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 30
  9. 2023Data-driven Traffic Simulation: A Comprehensive Reviewcited by 29
  10. 2023Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviorscited by 25
  11. 2024Is Your LiDAR Placement Optimized for 3D Scene Understanding?cited by 25
  12. 2024Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehensioncited by 23
  13. 2025SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Drivingcited by 21
  14. 2024ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zonescited by 20
  15. 2025Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Drivingcited by 19
  16. 2023Enhancing Traffic Object Detection in Variable Illumination with RGB-Event Fusioncited by 16
  17. 2024FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Drivingcited by 14
  18. 2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directionscited by 14
  19. 2023ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulationcited by 13
  20. 2024CarDreamer: Open-Source Learning Platform for World Model based Autonomous Drivingcited by 13
  21. 2025DistillDrive: End-to-End Multi-Mode Autonomous Driving Distillation by Isomorphic Hetero-Source Planning Modelcited by 13
  22. 2026Bridging Scene Generation and Planning: Driving with World Model via Unifying Vision and Motion Representationcited by 13
  23. 2023End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023cited by 11
  24. 2024Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspectivecited by 11
  25. 2024Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptationcited by 10
  26. 2023Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planningcited by 9
  27. 2023FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusioncited by 7
  28. 2024HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarioscited by 7
  29. 2025LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarioscited by 7
  30. 2023Mind the map! Accounting for existing map information when estimating online HDMaps from sensorcited by 6
  31. 2024Autonomous Driving Small-Scale Cars: A Survey of Recent Developmentcited by 6
  32. 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
  33. 2024Integration of cognitive tasks into artificial general intelligence test for large modelscited by 5
  34. 2024PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving Environmentscited by 5
  35. 2024Boosting Visual Recognition in Real-world Degradations via Unsupervised Feature Enhancement Module with Deep Channel Priorcited by 5
  36. 2024Enhancing Safety in Reinforcement Learning with Human Feedback via Rectified Policy Optimizationcited by 5
  37. 2023Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-makingcited by 4
  38. 2024A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Drivingcited by 4

50 most cited of 112 All 112 (JSON)

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  1. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
  2. 2026Planning-Oriented End-to-End Autonomous Driving: Architectures, Evaluation, and Emerging Paradigms
  3. 2025Benchmarking Autonomous Driving Planners Across Leaderboards: A Unified CARLA-Based Evaluation
  4. 2022Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning
  5. 2026A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform
  6. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
  7. 2026Post-Training in End-to-End Autonomous Driving
  8. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions