Data to 7 Oct 2026

Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data

arXiv 1910.08233 ↗First posted 18 Oct 2019cs.CV cs.LG cs.ROcited by 104

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Abstract (arXiv)

In this paper, we tackle the problem of relational behavior forecasting from sensor data. Towards this goal, we propose a novel spatially-aware graph neural network (SpAGNN) that models the interactions between agents in the scene. Specifically, we exploit a convolutional neural network to detect the actors and compute their initial states. A graph neural network then iteratively updates the actor states via a message passing process. Inspired by Gaussian belief propagation, we design the messages to be spatially-transformed parameters of the output distributions from neighboring agents. Our model is fully differentiable, thus enabling end-to-end training. Importantly, our probabilistic predictions can model uncertainty at the trajectory level. We demonstrate the effectiveness of our approach by achieving significant improvements over the state-of-the-art on two real-world self-driving datasets: ATG4D and nuScenes.

Key facts. Cited by 104 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Oct 2019.

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Citing papers per year: 2021: 33; 2022: 26; 2023: 20; 2024: 12; 2025: 8; 2026: 5.

104 papers.

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2022Planning-oriented Autonomous Drivingcited by 1,068
2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 532
2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 372
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
2021FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Camerascited by 239
2021MP3: A Unified Model to Map, Perceive, Predict and Plancited by 211
2021TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviorscited by 192
2021Scene Transformer: A unified architecture for predicting multiple agent trajectoriescited by 165
2023MotionLM: Multi-Agent Motion Forecasting as Language Modelingcited by 154

38 more

2023MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Queryingcited by 139
2021LaneRCNN: Distributed Representations for Graph-Centric Motion Forecastingcited by 112
2021Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Taskscited by 100
2021LookOut: Diverse Multi-Future Prediction and Planning for Self-Drivingcited by 96
2023Data Distillation: A Surveycited by 86
2023The Waymo Open Sim Agents Challengecited by 86
2022M2I: From Factored Marginal Trajectory Prediction to Interactive Predictioncited by 81
2022BITS: Bi-level Imitation for Traffic Simulationcited by 80
2022GoRela: Go Relative for Viewpoint-Invariant Motion Forecastingcited by 68
2022ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planningcited by 63
2021On Exposing the Challenging Long Tail in Future Prediction of Traffic Actorscited by 48
2022StretchBEV: Stretching Future Instance Prediction Spatially and Temporallycited by 47
2021Boost then Convolve: Gradient Boosting Meets Graph Neural Networkscited by 42
2022FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphscited by 41
2022JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Drivingcited by 40
2021Spectral Temporal Graph Neural Network for Trajectory Predictioncited by 36
2023Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encodingcited by 32
2022Behavioral Intention Prediction in Driving Scenes: A Surveycited by 30
2023TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Drivingcited by 30
2023Implicit Occupancy Flow Fields for Perception and Prediction in Self-Drivingcited by 30
2023Data-driven Traffic Simulation: A Comprehensive Reviewcited by 29
2023WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecastingcited by 28
2022Graph Neural Networks in IoT: A Surveycited by 27
20223D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphscited by 24
2023PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye Viewcited by 24
2021Learning by Watchingcited by 22
2021TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Trackingcited by 21
2021FloMo: Tractable Motion Prediction with Normalizing Flowscited by 21
2022MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Drivingcited by 21
2021Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertaintycited by 19
2021Deep Structured Reactive Planningcited by 18
2021MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Datacited by 18
2022CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationshipscited by 18
2023DEGREE: Decomposition Based Explanation For Graph Neural Networkscited by 18
2021Propagating State Uncertainty Through Trajectory Forecastingcited by 17
2023RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understandingcited by 17
2023EDA: Evolving and Distinct Anchors for Multimodal Motion Predictioncited by 17
2021SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUscited by 16

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