mlsanityData to 7 Oct 2026

Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations

arXiv 2008.05930 ↗First posted 13 Aug 2020cs.RO cs.AI cs.CVcited by 150

Not on the map. Why?

Cited by 86 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper we propose a novel end-to-end learnable network that performs joint perception, prediction and motion planning for self-driving vehicles and produces interpretable intermediate representations. Unlike existing neural motion planners, our motion planning costs are consistent with our perception and prediction estimates. This is achieved by a novel differentiable semantic occupancy representation that is explicitly used as cost by the motion planning process. Our network is learned end-to-end from human demonstrations. The experiments in a large-scale manual-driving dataset and closed-loop simulation show that the proposed model significantly outperforms state-of-the-art planners in imitating the human behaviors while producing much safer trajectories.

Key facts. Cited by 150 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 22; 2022: 17; 2023: 39; 2024: 29; 2025: 31; 2026: 11.

150 papers.

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  1. 2022Planning-oriented Autonomous Drivingcited by 1,068
  2. 2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 613
  3. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  4. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
  5. 2022ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learningcited by 365
  6. 2023GPT-Driver: Learning to Drive with GPTcited by 344
  7. 2024NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarkingcited by 343
  8. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baselinecited by 247
  9. 2022VectorMapNet: End-to-end Vectorized HD Map Learningcited by 243
  10. 2023Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?cited by 242
  11. 2024SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representationcited by 239
  12. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 238
38 more
  1. 2021MP3: A Unified Model to Map, Perceive, Predict and Plancited by 211
  2. 2023Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenescited by 191
  3. 2023LanguageMPC: Large Language Models as Decision Makers for Autonomous Drivingcited by 188
  4. 2021NEAT: Neural Attention Fields for End-to-End Autonomous Drivingcited by 174
  5. 2023Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Drivingcited by 168
  6. 2023A Language Agent for Autonomous Drivingcited by 161
  7. 2024Enhancing End-to-End Autonomous Driving with Latent World Modelcited by 161
  8. 2023Drive Like a Human: Rethinking Autonomous Driving with Large Language Modelscited by 158
  9. 2025End-to-End Driving with Online Trajectory Evaluation via BEV World Modelcited by 145
  10. 2021Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Priorcited by 136
  11. 2023LLM4Drive: A Survey of Large Language Models for Autonomous Drivingcited by 136
  12. 2021AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehiclescited by 132
  13. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  14. 2024VLP: Vision Language Planning for Autonomous Drivingcited by 125
  15. 2023Vision Language Models in Autonomous Driving: A Survey and Outlookcited by 124
  16. 2021HOME: Heatmap Output for future Motion Estimationcited by 122
  17. 2021Learning to drive from a world on railscited by 114
  18. 2021LaneRCNN: Distributed Representations for Graph-Centric Motion Forecastingcited by 112
  19. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
  20. 2022PlanT: Explainable Planning Transformers via Object-Level Representationscited by 105
  21. 2023Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusioncited by 105
  22. 2024Towards learning-based planning:The nuPlan benchmark for real-world autonomous drivingcited by 103
  23. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  24. 2021LookOut: Diverse Multi-Future Prediction and Planning for Self-Drivingcited by 96
  25. 2022KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradientscited by 95
  26. 2024RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Modelcited by 91
  27. 2022Differentiable Raycasting for Self-supervised Occupancy Forecastingcited by 83
  28. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  29. 2023ADAPT: Action-aware Driving Caption Transformercited by 78
  30. 2023Point Cloud Forecasting as a Proxy for 4D Occupancy Forecastingcited by 76
  31. 2025A Survey on Vision-Language-Action Models for Autonomous Drivingcited by 72
  32. 2022Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Drivingcited by 71
  33. 2023FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Drivingcited by 67
  34. 2024Driving in the Occupancy World: Vision-Centric 4D Occupancy Forecasting and Planning via World Models for Autonomous Drivingcited by 67
  35. 2025A Survey of World Models for Autonomous Drivingcited by 60
  36. 2023LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoningcited by 59
  37. 2023Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Mapscited by 58
  38. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 52

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