用未来轨迹生成驾驶行为解释，让自动驾驶决策更透明。
Explanation for Trajectory Planning using Multi-modal Large Language Model for Autonomous Driving
- 输入车辆未来规划轨迹，生成行为解释
- 新数据集支持多模态轨迹-文本对训练
- 适合关注自动驾驶可解释性的研究者
近期发展端到端自动驾驶模型缺乏从感知到控制决策过程的可解释性，导致乘客焦虑。为缓解此问题，构建能输出描述自车未来行为及其原因的文本的模型有效。然而，现有方法因仅以瞬时控制信号为输入，生成的推理文本未能充分反映自车未来规划。本研究提出一种新推理模型，以自车未来规划轨迹为输入，利用新采集的数据集进行训练，解决了该局限性。
原文摘要 · Abstract (English)
End-to-end style autonomous driving models have been developed recently. These models lack interpretability of decision-making process from perception to control of the ego vehicle, resulting in anxiety for passengers. To alleviate it, it is effective to build a model which outputs captions describing future behaviors of the ego vehicle and their reason. However, the existing approaches generate reasoning text that inadequately reflects the future plans of the ego vehicle, because they train models to output captions using momentary control signals as inputs. In this study, we propose a reasoning model that takes future planning trajectories of the ego vehicle as inputs to solve this limitation with the dataset newly collected.
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