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FollowMe: Vehicle Behaviour Prediction in Autonomous Vehicle Settings

arXiv 2304.06121 ↗First posted 12 Apr 2023cs.RO cs.CVcited by 0

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Abstract (arXiv)

An ego vehicle following a virtual lead vehicle planned route is an essential component when autonomous and non-autonomous vehicles interact. Yet, there is a question about the driver's ability to follow the planned lead vehicle route. Thus, predicting the trajectory of the ego vehicle route given a lead vehicle route is of interest. We introduce a new dataset, the FollowMe dataset, which offers a motion and behavior prediction problem by answering the latter question of the driver's ability to follow a lead vehicle. We also introduce a deep spatio-temporal graph model FollowMe-STGCNN as a baseline for the dataset. In our experiments and analysis, we show the design benefits of FollowMe-STGCNN in capturing the interactions that lie within the dataset. We contrast the performance of FollowMe-STGCNN with prior motion prediction models showing the need to have a different design mechanism to address the lead vehicle following settings.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 12 Apr 2023.

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References · 16

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2013Auto-Encoding Variational Bayescited by 3,980
  4. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  5. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  6. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 532
  7. 2020Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Predictioncited by 324
  8. 2020It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Predictioncited by 235
  9. 2018Implicit Maximum Likelihood Estimationcited by 52
  10. 2021On Exposing the Challenging Long Tail in Future Prediction of Traffic Actorscited by 48
  11. 2019Human-Like Autonomous Car-Following Model with Deep Reinforcement Learningcited by 47
  12. 2022Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimationcited by 40
4 more
  1. 2020Augmented Reality-Based Advanced Driver-Assistance System for Connected Vehiclescited by 4
  2. 2020Toward Adaptive Trust Calibration for Level 2 Driving Automationcited by 4
  3. 2020Self-Growing Spatial Graph Network for Context-Aware Pedestrian Trajectory Predictioncited by 3
  4. 2021Clustering Human Trust Dynamics for Customized Real-time Predictioncited by 2

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