Data to 7 Oct 2026

Learning Lane Graph Representations for Motion Forecasting

arXiv 2007.13732 ↗First posted 27 Jul 2020cs.CVcited by 380

Not on the map. Why?

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

Abstract (arXiv)

We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions. Instead of encoding vectorized maps as raster images, we construct a lane graph from raw map data to explicitly preserve the map structure. To capture the complex topology and long range dependencies of the lane graph, we propose LaneGCN which extends graph convolutions with multiple adjacency matrices and along-lane dilation. To capture the complex interactions between actors and maps, we exploit a fusion network consisting of four types of interactions, actor-to-lane, lane-to-lane, lane-to-actor and actor-to-actor. Powered by LaneGCN and actor-map interactions, our model is able to predict accurate and realistic multi-modal trajectories. Our approach significantly outperforms the state-of-the-art on the large scale Argoverse motion forecasting benchmark.

Key facts. Cited by 380 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 48; 2022: 69; 2023: 92; 2024: 76; 2025: 64; 2026: 31.

380 papers.

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2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 701
2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 613
2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 532
2021DenseTNT: End-to-end Trajectory Prediction from Dense Goal Setscited by 292
2024VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planningcited by 287
2022Wayformer: Motion Forecasting via Simple & Efficient Attention Networkscited by 279
2022Motion Transformer with Global Intention Localization and Local Movement Refinementcited by 267
2021MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Predictioncited by 246
2024GenAD: Generative End-to-End Autonomous Drivingcited by 225
2021Multimodal Motion Prediction with Stacked Transformerscited by 207
2021Scene Transformer: A unified architecture for predicting multiple agent trajectoriescited by 165
2023Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Researchcited by 158

38 more

2023MotionLM: Multi-Agent Motion Forecasting as Language Modelingcited by 154
2021GOHOME: Graph-Oriented Heatmap Output for future Motion Estimationcited by 146
2023StreamMapNet: Streaming Mapping Network for Vectorized Online HD Map Constructioncited by 143
2023MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Queryingcited by 139
2021Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Priorcited by 136
2021Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Predictioncited by 131
2025DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Drivingcited by 125
2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarioscited by 124
2021HOME: Heatmap Output for future Motion Estimationcited by 122
2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradientscited by 122
2022ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queriescited by 116
2021LaneRCNN: Distributed Representations for Graph-Centric Motion Forecastingcited by 112
2021Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversalscited by 112
2022HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encodingcited by 109
2024Towards learning-based planning:The nuPlan benchmark for real-world autonomous drivingcited by 103
2021THOMAS: Trajectory Heatmap Output with learned Multi-Agent Samplingcited by 101
2021Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Taskscited by 100
2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
2023EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoningcited by 97
2021TPCN: Temporal Point Cloud Networks for Motion Forecastingcited by 94
2022Remember Intentions: Retrospective-Memory-based Trajectory Predictioncited by 86
2021SceneGen: Learning to Generate Realistic Traffic Scenescited by 83
2022M2I: From Factored Marginal Trajectory Prediction to Interactive Predictioncited by 81
2021SimNet: Learning Reactive Self-driving Simulations from Real-world Observationscited by 77
2023Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoderscited by 72
2022GoRela: Go Relative for Viewpoint-Invariant Motion Forecastingcited by 68
2023FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Drivingcited by 67
2021LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agentscited by 64
2021Learning to Predict Vehicle Trajectories with Model-based Planningcited by 63
2024NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Drivingcited by 61
2023LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Drivingcited by 57
2022Graph Filters for Signal Processing and Machine Learning on Graphscited by 53
2023Graph-based Topology Reasoning for Driving Scenescited by 53
2024UniTraj: A Unified Framework for Scalable Vehicle Trajectory Predictioncited by 51
2023LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraintscited by 50
2022GANet: Goal Area Network for Motion Forecastingcited by 49
2021On Exposing the Challenging Long Tail in Future Prediction of Traffic Actorscited by 48
2022StretchBEV: Stretching Future Instance Prediction Spatially and Temporallycited by 47

50 most cited of 380 All 380 (JSON)