mlsanityData to 7 Oct 2026

Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network

arXiv 2103.16273 ↗First posted 30 Mar 2021cs.CV cs.ROcited by 6

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Abstract (arXiv)

Predicting future trajectories of surrounding obstacles is a crucial task for autonomous driving cars to achieve a high degree of road safety. There are several challenges in trajectory prediction in real-world traffic scenarios, including obeying traffic rules, dealing with social interactions, handling traffic of multi-class movement, and predicting multi-modal trajectories with probability. Inspired by people's natural habit of navigating traffic with attention to their goals and surroundings, this paper presents a unique dynamic graph attention network to solve all those challenges. The network is designed to model the dynamic social interactions among agents and conform to traffic rules with a semantic map. By extending the anchor-based method to multiple types of agents, the proposed method can predict multi-modal trajectories with probabilities for multi-class movements using a single model. We validate our approach on the proprietary autonomous driving dataset for the logistic delivery scenario and two publicly available datasets. The results show that our method outperforms state-of-the-art techniques and demonstrates the potential for trajectory prediction in real-world traffic.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 30 Mar 2021.

Builds on

14 of 24 references.

References · 14

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Graph Attention Networkscited by 8,854
  3. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  4. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
  5. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  6. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
  7. 2020Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Predictioncited by 324
  8. 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
  9. 2018SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraintscited by 296
  10. 2018Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networkscited by 289
  11. 2020It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Predictioncited by 235
  12. 2019Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networkscited by 228
2 more
  1. 2018TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactionscited by 82
  2. 2017CAR-Net: Clairvoyant Attentive Recurrent Networkcited by 34

Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 1; 2024: 1.

6 papers.

  1. 2022Graph Neural Networks in IoT: A Surveycited by 27
  2. 2022QML for Argoverse 2 Motion Forecasting Challengecited by 7
  3. 2022Exploring Attention GAN for Vehicle Motion Predictioncited by 6
  4. 2023Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharingcited by 3
  5. 2022Exploring Map-based Features for Efficient Attention-based Vehicle Motion Predictioncited by 1
  6. 2024Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Modelscited by 0