Improving Consistency in Vehicle Trajectory Prediction Through Preference Optimization

TL;DR

This paper enhances vehicle trajectory prediction consistency in autonomous driving by fine-tuning models with preference optimization, improving scene coherence without sacrificing accuracy or increasing inference complexity.

Contribution

It introduces a novel preference optimization approach for fine-tuning trajectory models, significantly improving scene consistency in multi-agent scenarios.

Findings

Improved scene consistency across three datasets.

Minimal loss in trajectory prediction accuracy.

No additional computational overhead at inference.

Abstract

Trajectory prediction is an essential step in the pipeline of an autonomous vehicle. Inaccurate or inconsistent predictions regarding the movement of agents in its surroundings lead to poorly planned maneuvers and potentially dangerous situations for the end-user. Current state-of-the-art deep-learning-based trajectory prediction models can achieve excellent accuracy on public datasets. However, when used in more complex, interactive scenarios, they often fail to capture important interdependencies between agents, leading to inconsistent predictions among agents in the traffic scene. Inspired by the efficacy of incorporating human preference into large language models, this work fine-tunes trajectory prediction models in multi-agent settings using preference optimization. By taking as input automatically calculated preference rankings among predicted futures in the fine-tuning process, our experiments--using state-of-the-art models on three separate datasets--show that we are able to significantly improve scene consistency while minimally sacrificing trajectory prediction accuracy and without adding any excess computational requirements at inference time.