Data to 7 Oct 2026

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

arXiv 2007.12036 ↗First posted 23 Jul 2020cs.CV cs.LG cs.ROcited by 116

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Abstract (arXiv)

In order to plan a safe maneuver an autonomous vehicle must accurately perceive its environment, and understand the interactions among traffic participants. In this paper, we aim to learn scene-consistent motion forecasts of complex urban traffic directly from sensor data. In particular, we propose to characterize the joint distribution over future trajectories via an implicit latent variable model. We model the scene as an interaction graph and employ powerful graph neural networks to learn a distributed latent representation of the scene. Coupled with a deterministic decoder, we obtain trajectory samples that are consistent across traffic participants, achieving state-of-the-art results in motion forecasting and interaction understanding. Last but not least, we demonstrate that our motion forecasts result in safer and more comfortable motion planning.

Key facts. Cited by 116 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 28; 2022: 23; 2023: 29; 2024: 17; 2025: 15; 2026: 4.

116 papers.

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2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 532
2023LMDrive: Closed-Loop End-to-End Driving with Large Language Modelscited by 301
2021DenseTNT: End-to-end Trajectory Prediction from Dense Goal Setscited by 292
2022Motion Transformer with Global Intention Localization and Local Movement Refinementcited by 267
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
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
2022ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queriescited by 116

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2021LaneRCNN: Distributed Representations for Graph-Centric Motion Forecastingcited by 112
2022HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encodingcited by 109
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
2021LookOut: Diverse Multi-Future Prediction and Planning for Self-Drivingcited by 96
2023ReasonNet: End-to-End Driving with Temporal and Global Reasoningcited by 92
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
2021Learning to Predict Vehicle Trajectories with Model-based Planningcited by 63
2022ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planningcited by 63
2023TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Predictioncited by 56
2023Leveraging Future Relationship Reasoning for Vehicle Trajectory Predictioncited by 47
2021Auto4D: Learning to Label 4D Objects from Sequential Point Cloudscited by 42
2023Traj-MAE: Masked Autoencoders for Trajectory Predictioncited 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
2021Divide-and-Conquer for Lane-Aware Diverse Trajectory Predictioncited by 37
2024HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attentioncited by 37
2021Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulationcited by 32
2023Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encodingcited by 32
2023Implicit Occupancy Flow Fields for Perception and Prediction in Self-Drivingcited by 30
2021PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulationcited by 29
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
2022Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Expertscited by 28
2024SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Drivingcited by 27
2024CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learningcited by 26
2023Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023cited by 25
2023Learning Realistic Traffic Agents in Closed-loopcited by 24
2022The Neural Process Family: Survey, Applications and Perspectivescited by 23
2021Deep Multi-Task Learning for Joint Localization, Perception, and Predictioncited 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
2022LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Predictioncited by 19
2021Deep Structured Reactive Planningcited by 18
2021Unlimited Neighborhood Interaction for Heterogeneous Trajectory Predictioncited by 17

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