RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction

Schmidt J, Huissel P, Wiederer J, Jordan J, Belagiannis V, Dietmayer K (2023)


Publication Type: Conference contribution

Publication year: 2023

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2023-June

Conference Proceedings Title: IEEE Intelligent Vehicles Symposium, Proceedings

Event location: Anchorage, AK, USA

ISBN: 9798350346916

DOI: 10.1109/IV55152.2023.10186587

Abstract

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.

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How to cite

APA:

Schmidt, J., Huissel, P., Wiederer, J., Jordan, J., Belagiannis, V., & Dietmayer, K. (2023). RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction. In IEEE Intelligent Vehicles Symposium, Proceedings. Anchorage, AK, USA: Institute of Electrical and Electronics Engineers Inc..

MLA:

Schmidt, Julian, et al. "RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction." Proceedings of the 34th IEEE Intelligent Vehicles Symposium, IV 2023, Anchorage, AK, USA Institute of Electrical and Electronics Engineers Inc., 2023.

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