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arXiv · 2410.09681

LoRD: Adapting Differentiable Driving Policies to Distribution Shifts

Christopher Diehl·Peter Karkus·Sushant Veer·Marco Pavone·Torsten Bertram
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Abstract

Distribution shifts between operational domains can severely affect the performance of learned models in self-driving vehicles (SDVs). While this is a well-established problem, prior work has mostly explored naive solutions such as fine-tuning, focusing on the motion prediction task. In this work, we explore novel adaptation strategies for differentiable autonomy stacks consisting of prediction, planning, and control, perform evaluation in closed-loop, and investigate the often-overlooked issue of catastrophic forgetting. Specifically, we introduce two simple yet effective techniques: a low-rank residual decoder (LoRD) and multi-task fine-tuning. Through experiments across three models conducted on two real-world autonomous driving datasets (nuPlan, exiD), we demonstrate the effectiveness of our methods and highlight a significant performance gap between open-loop and closed-loop evaluation in prior approaches. Our approach improves forgetting by up to 23.33% and the closed-loop OOD driving score by 9.93% in comparison to standard fine-tuning.

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Christopher Diehl, Peter Karkus, Sushant Veer, Marco Pavone, Torsten Bertram. 2024-10-13. LoRD: Adapting Differentiable Driving Policies to Distribution Shifts. https://arxiv.org/abs/2410.09681

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