A CVAE with multi-head attention and traffic-signal encoding can be unrolled in a closed loop to simulate intersection traffic, with new safety-focused evaluation metrics; the model improves on some metrics but worsens red-light violations.
DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets
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abstract
Due to the stochasticity of human behaviors, predicting the future trajectories of road agents is challenging for autonomous driving. Recently, goal-based multi-trajectory prediction methods are proved to be effective, where they first score over-sampled goal candidates and then select a final set from them. However, these methods usually involve goal predictions based on sparse pre-defined anchors and heuristic goal selection algorithms. In this work, we propose an anchor-free and end-to-end trajectory prediction model, named DenseTNT, that directly outputs a set of trajectories from dense goal candidates. In addition, we introduce an offline optimization-based technique to provide multi-future pseudo-labels for our final online model. Experiments show that DenseTNT achieves state-of-the-art performance, ranking 1st on the Argoverse motion forecasting benchmark and being the 1st place winner of the 2021 Waymo Open Dataset Motion Prediction Challenge.
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IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections
A CVAE with multi-head attention and traffic-signal encoding can be unrolled in a closed loop to simulate intersection traffic, with new safety-focused evaluation metrics; the model improves on some metrics but worsens red-light violations.