mlsanityData to 7 Oct 2026

IntentNet: Learning to Predict Intention from Raw Sensor Data

arXiv 2101.07907 ↗First posted 20 Jan 2021cs.RO cs.AI cs.CVcited by 158

Not on the map. Why?

Cited by 27 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants. We define intent as a combination of discrete high-level behaviors as well as continuous trajectories describing future motion. In this paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reducing reaction time in self-driving applications.

Key facts. Cited by 158 papers in the map (data through 7 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 20 Jan 2021.

Builds on

21 of 37 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  5. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  6. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  7. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  8. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  9. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
  10. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  11. 2017Understanding the Effective Receptive Field in Deep Convolutional Neural Networkscited by 549
  12. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
9 more
  1. 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
  2. 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
  3. 2020Deep Continuous Fusion for Multi-Sensor 3D Object Detectioncited by 263
  4. 2020Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Netcited by 228
  5. 2016Vehicle Detection from 3D Lidar Using Fully Convolutional Networkcited by 105
  6. 2016Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networkscited by 72
  7. 2018Probabilistic Prediction of Vehicle Semantic Intention and Motioncited by 31
  8. 2017Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labelingcited by 30
  9. 2016Knowledge Transfer for Scene-specific Motion Predictioncited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 34; 2022: 43; 2023: 34; 2024: 23; 2025: 16; 2026: 8.

158 papers.

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  1. 2022Planning-oriented Autonomous Drivingcited by 1,068
  2. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 532
  3. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
  4. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 372
  5. 2021DenseTNT: End-to-end Trajectory Prediction from Dense Goal Setscited by 292
  6. 2022Wayformer: Motion Forecasting via Simple & Efficient Attention Networkscited by 279
  7. 2022Motion Transformer with Global Intention Localization and Local Movement Refinementcited by 267
  8. 2021MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Predictioncited by 246
  9. 2021FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Camerascited by 239
  10. 2024SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representationcited by 239
  11. 2023A Language Agent for Autonomous Drivingcited by 161
  12. 2023MotionLM: Multi-Agent Motion Forecasting as Language Modelingcited by 154
38 more
  1. 2023MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Queryingcited by 139
  2. 2022Learning from All Vehiclescited by 135
  3. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  4. 2022GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoningcited by 128
  5. 2022ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queriescited by 116
  6. 2022PlanT: Explainable Planning Transformers via Object-Level Representationscited by 105
  7. 2023EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoningcited by 97
  8. 2021TPCN: Temporal Point Cloud Networks for Motion Forecastingcited by 94
  9. 2023ReasonNet: End-to-End Driving with Temporal and Global Reasoningcited by 92
  10. 2023ADAPT: Action-aware Driving Caption Transformercited by 78
  11. 2022GoRela: Go Relative for Viewpoint-Invariant Motion Forecastingcited by 68
  12. 2021Learning to Predict Vehicle Trajectories with Model-based Planningcited by 63
  13. 2022Occupancy Flow Fields for Motion Forecasting in Autonomous Drivingcited by 62
  14. 2022Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulationcited by 61
  15. 2022Motion Inspired Unsupervised Perception and Prediction in Autonomous Drivingcited by 59
  16. 2021LOKI: Long Term and Key Intentions for Trajectory Predictioncited by 57
  17. 2021Procedure Planning in Instructional Videos via Contextual Modeling and Model-based Policy Learningcited by 53
  18. 2021Scalable Scene Flow from Point Clouds in the Real Worldcited by 49
  19. 2022GANet: Goal Area Network for Motion Forecastingcited by 49
  20. 2023HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Drivingcited by 48
  21. 2022StretchBEV: Stretching Future Instance Prediction Spatially and Temporallycited by 47
  22. 2021Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networkscited by 42
  23. 2022JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Drivingcited by 40
  24. 2024UnO: Unsupervised Occupancy Fields for Perception and Forecastingcited by 40
  25. 2021Vehicle trajectory prediction works, but not everywherecited by 39
  26. 2023ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptationcited by 39
  27. 2025HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decodercited by 35
  28. 2021Topology Preserving Local Road Network Estimation from Single Onboard Camera Imagecited by 34
  29. 2023PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Drivingcited by 33
  30. 2022SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Drivingcited by 31
  31. 2022Behavioral Intention Prediction in Driving Scenes: A Surveycited by 30
  32. 2023TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Drivingcited by 30
  33. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  34. 2022Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoningcited by 28
  35. 2022Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Predictioncited by 28
  36. 2023WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecastingcited by 28
  37. 2023Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networkscited by 27
  38. 2022Trust, but Verify: Cross-Modality Fusion for HD Map Change Detectioncited by 25

50 most cited of 158 All 158 (JSON)