mlsanityData to 7 Oct 2026

End-to-end Interpretable Neural Motion Planner

arXiv 2101.06679 ↗First posted 17 Jan 2021cs.CV cs.ROcited by 240

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

Abstract (arXiv)

In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. We then sample a set of diverse physically possible trajectories and choose the one with the minimum learned cost. Importantly, our cost volume is able to naturally capture multi-modality. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America. Our experiments show that the learned cost volume can generate safer planning than all the baselines.

Key facts. Cited by 240 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 18 of its 36 references that are in the map. First posted on arXiv 17 Jan 2021.

Builds on

18 of 36 references.

References · 18

  1. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  2. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  3. 2016Pyramid Scene Parsing Networkcited by 2,472
  4. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  5. 2016End to End Learning for Self-Driving Carscited by 1,000
  6. 2017Understanding the Effective Receptive Field in Deep Convolutional Neural Networkscited by 549
  7. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  8. 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
  9. 2018Learning to Drive in a Daycited by 266
  10. 2016A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehiclescited by 251
  11. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 229
  12. 2020Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Netcited by 228
6 more
  1. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 173
  2. 2018Baidu Apollo EM Motion Plannercited by 99
  3. 2018Driving Policy Transfer via Modularity and Abstractioncited by 91
  4. 2017Virtual to Real Reinforcement Learning for Autonomous Drivingcited by 79
  5. 2018Conditional Affordance Learning for Driving in Urban Environmentscited by 70
  6. 2018Search-based optimal motion planning for automated drivingcited by 24

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 38; 2022: 39; 2023: 47; 2024: 55; 2025: 43; 2026: 18.

240 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. 2021NuPlan: A closed-loop ML-based planning benchmark for autonomous vehiclescited by 485
  4. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  5. 2024DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Modelscited by 482
  6. 2022ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learningcited by 365
  7. 2023GPT-Driver: Learning to Drive with GPTcited by 344
  8. 2024NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarkingcited by 343
  9. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  10. 2022Wayformer: Motion Forecasting via Simple & Efficient Attention Networkscited by 279
  11. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baselinecited by 247
  12. 2021MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Predictioncited by 246
38 more
  1. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 238
  2. 2023OccWorld: Learning a 3D Occupancy World Model for Autonomous Drivingcited by 230
  3. 2024GenAD: Generative End-to-End Autonomous Drivingcited by 225
  4. 2021MP3: A Unified Model to Map, Perceive, Predict and Plancited by 211
  5. 2022Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformercited by 211
  6. 2023MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusioncited by 210
  7. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 191
  8. 2023Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenescited by 191
  9. 2021NEAT: Neural Attention Fields for End-to-End Autonomous Drivingcited by 174
  10. 2023Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Drivingcited by 168
  11. 2021Scene Transformer: A unified architecture for predicting multiple agent trajectoriescited by 165
  12. 2023A Language Agent for Autonomous Drivingcited by 161
  13. 2024Enhancing End-to-End Autonomous Driving with Latent World Modelcited by 161
  14. 2024Behavior Generation with Latent Actionscited by 159
  15. 2024Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Drivingcited by 156
  16. 2021Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationcited by 141
  17. 2023GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Drivingcited by 141
  18. 2021Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Priorcited by 136
  19. 2022Learning from All Vehiclescited by 135
  20. 2024VLP: Vision Language Planning for Autonomous Drivingcited by 125
  21. 2025DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Drivingcited by 125
  22. 2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradientscited by 122
  23. 2021Learning to drive from a world on railscited by 114
  24. 2021Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversalscited by 112
  25. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
  26. 2022PlanT: Explainable Planning Transformers via Object-Level Representationscited by 105
  27. 2024Towards learning-based planning:The nuPlan benchmark for real-world autonomous drivingcited by 103
  28. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  29. 2022KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradientscited by 95
  30. 2021TPCN: Temporal Point Cloud Networks for Motion Forecastingcited by 94
  31. 2025Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillationcited by 93
  32. 2024RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Modelcited by 91
  33. 2025Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Drivingcited by 84
  34. 2022Differentiable Raycasting for Self-supervised Occupancy Forecastingcited by 83
  35. 2023ADAPT: Action-aware Driving Caption Transformercited by 78
  36. 2021SimNet: Learning Reactive Self-driving Simulations from Real-world Observationscited by 77
  37. 2023Point Cloud Forecasting as a Proxy for 4D Occupancy Forecastingcited by 76
  38. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73

50 most cited of 240 All 240 (JSON)

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  1. 2025PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving
  2. 2021ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles
  3. 2025World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model
  4. 2026Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
  5. 2024Path Planning based on 2D Object Bounding-box
  6. 2026Planning-Oriented End-to-End Autonomous Driving: Architectures, Evaluation, and Emerging Paradigms
  7. 2024DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba
  8. 2025DAP: A Discrete-token Autoregressive Planner for Autonomous Driving