Improving Consistency in Vehicle Trajectory Prediction Through Preference Optimization
This addresses safety and reliability issues for autonomous vehicles by enhancing prediction consistency in interactive settings, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of inconsistent trajectory predictions in multi-agent autonomous driving scenarios by fine-tuning models with preference optimization, resulting in significantly improved scene consistency with minimal accuracy loss across three datasets.
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.