RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction
This work addresses the problem of efficiently predicting traffic participant behavior conditioned on multiple autonomous vehicle plans for downstream planners, though it is incremental in applying set-based methods to this task.
The paper tackles conditional behavior prediction for autonomous vehicles by revisiting set-based trajectory prediction, achieving comparable performance to regression-based methods for unconditional prediction and enabling flexible trajectory prediction without runtime impact.
It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle. This allows the downstream planner to estimate the impact of its decisions. Recent approaches for conditional behavior prediction rely on a regression decoder, meaning that coordinates or polynomial coefficients are regressed. In this work we revisit set-based trajectory prediction, where the probability of each trajectory in a predefined trajectory set is determined by a classification model, and first-time employ it to the task of conditional behavior prediction. We propose RESET, which combines a new metric-driven algorithm for trajectory set generation with a graph-based encoder. For unconditional prediction, RESET achieves comparable performance to a regression-based approach. Due to the nature of set-based approaches, it has the advantageous property of being able to predict a flexible number of trajectories without influencing runtime or complexity. For conditional prediction, RESET achieves reasonable results with late fusion of the planned trajectory, which was not observed for regression-based approaches before. This means that RESET is computationally lightweight to combine with a planner that proposes multiple future plans of the autonomous vehicle, as large parts of the forward pass can be reused.