Data to 13 Oct 2026

Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting

arXiv 1910.03650 ↗First posted 8 Oct 2019cs.LG cs.AI cs.ROcited by 87

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Abstract (arXiv)

This paper presents a novel vehicle motion forecasting method based on multi-head attention. It produces joint forecasts for all vehicles on a road scene as sequences of multi-modal probability density functions of their positions. Its architecture uses multi-head attention to account for complete interactions between all vehicles, and long short-term memory layers for encoding and forecasting. It relies solely on vehicle position tracks, does not need maneuver definitions, and does not represent the scene with a spatial grid. This allows it to be more versatile than similar model while combining any forecasting capabilities, namely joint forecast with interactions, uncertainty estimation, and multi-modality. The resulting prediction likelihood outperforms state-of-the-art models on the same dataset.

Key facts. Cited by 87 papers in the map (data through 13 Oct 2026). First posted on arXiv 8 Oct 2019.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 15; 2022: 26; 2023: 21; 2024: 15; 2025: 6; 2026: 4.

87 papers.

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2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 704
2022Wayformer: Motion Forecasting via Simple & Efficient Attention Networkscited by 280
2022Motion Transformer with Global Intention Localization and Local Movement Refinementcited by 269
2021Multimodal Motion Prediction with Stacked Transformerscited by 208
2021GOHOME: Graph-Oriented Heatmap Output for future Motion Estimationcited by 146
2023MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Queryingcited by 140
2021HOME: Heatmap Output for future Motion Estimationcited by 122
2021LaneRCNN: Distributed Representations for Graph-Centric Motion Forecastingcited by 113
2021THOMAS: Trajectory Heatmap Output with learned Multi-Agent Samplingcited by 101
2021TPCN: Temporal Point Cloud Networks for Motion Forecastingcited by 94
2021Learning to Predict Vehicle Trajectories with Model-based Planningcited by 63
2022Occupancy Flow Fields for Motion Forecasting in Autonomous Drivingcited by 62

38 more

2022GANet: Goal Area Network for Motion Forecastingcited by 49
2023Leveraging Future Relationship Reasoning for Vehicle Trajectory Predictioncited by 48
2022Hierarchical Model-Based Imitation Learning for Planning in Autonomous Drivingcited by 43
2022JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Drivingcited by 40
2023ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptationcited by 40
2022SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Drivingcited by 32
2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
2023Data-driven Traffic Simulation: A Comprehensive Reviewcited by 29
2023Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networkscited by 28
2023WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecastingcited by 28
2022Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Modelscited by 27
2023SEPT: Towards Efficient Scene Representation Learning for Motion Predictioncited by 27
2022Bootstrap Motion Forecasting With Self-Consistent Constraintscited by 23
2023Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directionscited by 23
2022End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Mapscited by 22
2022CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attentioncited by 21
2021Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertaintycited by 19
2025Scaling Laws of Motion Forecasting and Planning -- Technical Reportcited by 19
2022Control-Aware Prediction Objectives for Autonomous Drivingcited by 18
2021Collaborative Uncertainty in Multi-Agent Trajectory Forecastingcited by 17
2022Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curriculacited by 17
2023EDA: Evolving and Distinct Anchors for Multimodal Motion Predictioncited by 17
2021DeepTrack: Lightweight Deep Learning for Vehicle Path Prediction in Highwayscited by 16
2023A Diffusion-Model of Joint Interactive Navigationcited by 16
2022StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Drivingcited by 14
2023Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environmentscited by 14
2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directionscited by 14
2021Trajectory Prediction with Linguistic Representationscited by 13
2022Concept Graph Neural Networks for Surgical Video Understandingcited by 13
2022Uncertainty estimation for Cross-dataset performance in Trajectory predictioncited by 13
2023IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Predictioncited by 13
2024AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Drivingcited by 13
2021Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Predictioncited by 12
2022RAP: Risk-Aware Prediction for Robust Planningcited by 12
2024MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Drivingcited by 12
2024SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction using Knowledge Graphscited by 11
2022TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictorcited by 10
2022Narrowing the Coordinate-frame Gap in Behavior Prediction Models: Distillation for Efficient and Accurate Scene-centric Motion Forecastingcited by 10

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