用人类偏好优化提升交通场景轨迹预测一致性
Improving Consistency in Vehicle Trajectory Prediction Through Preference Optimization
- 基于自动生成的未来轨迹偏好排序，微调多智能体预测模型
- 在三个数据集上显著提升场景一致性，精度损失极小
- 无需额外计算开销，适合实际自动驾驶系统部署
轨迹预测是自动驾驶系统中的关键环节。当前基于深度学习的预测模型在公开数据集上表现优异，但在复杂的交互场景中常因未能捕捉智能体间的依赖关系，导致预测结果不一致，进而影响路径规划并带来安全隐患。受大语言模型中人类偏好优化的启发，本文在多智能体设置下，利用自动计算的未来轨迹偏好排序对模型进行微调。实验表明，在三个不同数据集上，该方法显著提升了交通场景的整体一致性，同时几乎未牺牲轨迹预测精度，且推理时无额外计算开销。
原文摘要 · Abstract (English)
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.
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