LongComp: Long-Tail Compositional Zero-Shot Generalization for Robust Trajectory Prediction

TL;DR

This paper introduces a new evaluation framework for trajectory prediction in autonomous driving, focusing on long-tail, out-of-distribution scenarios, and proposes methods to improve model robustness and generalization.

Contribution

It presents a safety-informed scenario factorization framework and extends gating networks with auxiliary heads to enhance out-of-distribution generalization in trajectory prediction.

Findings

Induced OOD performance gaps of 5.0% and 14.7% in baseline models.

Proposed methods reduce OOD gaps to 2.8% and 11.5%.

Improved in-distribution performance alongside robustness enhancements.

Abstract

Methods for trajectory prediction in Autonomous Driving must contend with rare, safety-critical scenarios that make reliance on real-world data collection alone infeasible. To assess robustness under such conditions, we propose new long-tail evaluation settings that repartition datasets to create challenging out-of-distribution (OOD) test sets. We first introduce a safety-informed scenario factorization framework, which disentangles scenarios into discrete ego and social contexts. Building on analogies to compositional zero-shot image-labeling in Computer Vision, we then hold out novel context combinations to construct challenging closed-world and open-world settings. This process induces OOD performance gaps in future motion prediction of 5.0% and 14.7% in closed-world and open-world settings, respectively, relative to in-distribution performance for a state-of-the-art baseline. To improve generalization, we extend task-modular gating networks to operate within trajectory prediction models, and develop an auxiliary, difficulty-prediction head to refine internal representations. Our strategies jointly reduce the OOD performance gaps to 2.8% and 11.5% in the two settings, respectively, while still improving in-distribution performance.