Related papers: ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning

ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning

URL: http://arxiv.org/abs/2206.13387v1
Date: Sat, 18 Jun 2022 00:00:02 GMT
Title: ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning
Authors: Yuxiao Chen, Boris Ivanovic, and Marco Pavone
Abstract summary: Trajectory prediction is critical for autonomous systems that share environments with uncontrolled agents. We present ScePT, a policy planning-based trajectory prediction model. It explicitly enforces scene consistency and learns an agent interaction policy that can be used for conditional prediction.
Score: 32.71073060698739
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Trajectory prediction is a critical functionality of autonomous systems that share environments with uncontrolled agents, one prominent example being self-driving vehicles. Currently, most prediction methods do not enforce scene consistency, i.e., there are a substantial amount of self-collisions between predicted trajectories of different agents in the scene. Moreover, many approaches generate individual trajectory predictions per agent instead of joint trajectory predictions of the whole scene, which makes downstream planning difficult. In this work, we present ScePT, a policy planning-based trajectory prediction model that generates accurate, scene-consistent trajectory predictions suitable for autonomous system motion planning. It explicitly enforces scene consistency and learns an agent interaction policy that can be used for conditional prediction. Experiments on multiple real-world pedestrians and autonomous vehicle datasets show that ScePT} matches current state-of-the-art prediction accuracy with significantly improved scene consistency. We also demonstrate ScePT's ability to work with a downstream contingency planner.

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