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.

翻译：近年来，端到端式自动驾驶模型得到了发展。这些模型缺乏从感知到自车控制的决策过程可解释性，导致乘客产生焦虑感。为缓解此问题，构建能够输出描述自车未来行为及其原因的说明文本的模型是有效的。然而，现有方法生成的推理文本未能充分反映自车的未来规划，因为它们使用瞬时控制信号作为输入来训练模型生成说明文本。在本研究中，我们提出了一种推理模型，该模型以自车的未来规划轨迹作为输入，并利用新收集的数据集来解决这一局限性。