mlsanityData to 7 Oct 2026

Interpretable Long Term Waypoint-Based Trajectory Prediction Model

arXiv 2312.06219 ↗First posted 11 Dec 2023cs.AI stat.MLcited by 1

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Abstract (arXiv)

Predicting the future trajectories of dynamic agents in complex environments is crucial for a variety of applications, including autonomous driving, robotics, and human-computer interaction. It is a challenging task as the behavior of the agent is unknown and intrinsically multimodal. Our key insight is that the agents behaviors are influenced not only by their past trajectories and their interaction with their immediate environment but also largely with their long term waypoint (LTW). In this paper, we study the impact of adding a long-term goal on the performance of a trajectory prediction framework. We present an interpretable long term waypoint-driven prediction framework (WayDCM). WayDCM first predict an agent's intermediate goal (IG) by encoding his interactions with the environment as well as his LTW using a combination of a Discrete choice Model (DCM) and a Neural Network model (NN). Then, our model predicts the corresponding trajectories. This is in contrast to previous work which does not consider the ultimate intent of the agent to predict his trajectory. We evaluate and show the effectiveness of our approach on the Waymo Open dataset.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 29 references that are in the map. First posted on arXiv 11 Dec 2023.

Builds on

23 of 29 references.

References · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  4. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
  5. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 701
  6. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  7. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 532
  8. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
  9. 2020TNT: Target-driveN Trajectory Predictioncited by 345
  10. 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
  11. 2019CoverNet: Multimodal Behavior Prediction using Trajectory Setscited by 259
  12. 2019INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Mapscited by 231
11 more
  1. 2021GOHOME: Graph-Oriented Heatmap Output for future Motion Estimationcited by 146
  2. 2021LaneRCNN: Distributed Representations for Graph-Centric Motion Forecastingcited by 112
  3. 2021Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversalscited by 112
  4. 2021THOMAS: Trajectory Heatmap Output with learned Multi-Agent Samplingcited by 101
  5. 2022Human Trajectory Prediction via Neural Social Physicscited by 66
  6. 2021Interpretable Social Anchors for Human Trajectory Forecasting in Crowdscited by 44
  7. 2022MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Drivingcited by 21
  8. 2021GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Drivingcited by 19
  9. 2022MPA: MultiPath++ Based Architecture for Motion Predictioncited by 13
  10. 2022Aware of the History: Trajectory Forecasting with the Local Behavior Datacited by 8
  11. 2023Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarioscited by 3

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