Keywords: trajectory prediction, motion forecasting, transformers, latent variable models; Abstract: Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. We propose Latent Variable Sequential Set Transformers which Our method outperforms all previous models for both trajectory prediction and intention prediction tasks on the JAAD dataset and PIE dataset. such as graph neural networks or transformers, and the work in [15] proposes a behavior-aware trajectory generator. the trajectory direction of the green pedestrian is straight forward, and that of the red pedestrian deflects to avoid the collision with the green pedestrian. Pedestrian Trajectory Prediction using Context-Augmented Transformer Networks. Trajectory Prediction is the problem of predicting the short-term (1-3 seconds) and long-term (3-5 seconds) spatial coordinates of various road-agents such as cars, buses, pedestrians, rickshaws, and animals, etc. Multi-agent trajectory prediction is a fundamental problem in autonomous driving. A major challenge is to efficiently learn a representation that approximates the true joint distribution of contextual, social, and temporal information to enable planning. To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of other agents around it. Reframing Reinforcement Learning as Sequence Modeling with Transformers? Its . Trajectory Transformer Predicting motion of surrounding agents is critical to real-world applications of tactical path planning for autonomous driving. Notifications Fork 2; Star 8. In this work, we present a simple and yet strong baseline for uncertainty aware motion prediction based purely on transformer neural networks, which has . Trajectory Prediction for Autonomous Driving Using Spatial-Temporal ... This is a fundamental switch from the sequential step-by-step processing of LSTMs to the only-attention-based memory mechanisms of Transformers. An improved GAN with transformers for pedestrian trajectory prediction ... PDF Latent Variable Sequential Set Transformers for Joint Multi-agent ... not only the trajectory prediction [23] but also downstream planning tasks [4,53]. End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory ... Transformer has demonstrated outstanding performance in dealing with sequential data.

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