DriveLM

DriveLM: Driving with Graph Visual Question Answering.

We facilitate Perception, Prediction, Planning, Behavior, Motion tasks with human-written reasoning logic as a connection. We propose the task of GVQA to connect the QA pairs in a graph-style structure. To support this novel task, we provide the DriveLM-Data.
DriveLM-Data comprises two distinct components: DriveLM-nuScenes and DriveLM-CARLA. In the case of DriveLM-nuScenes, we construct our dataset based on the prevailing… See the full description on the dataset page: https://huggingface.co/datasets/OpenDriveLab/DriveLM.

DriveLM：基于图视觉问答的驾驶。本数据集旨在通过人类编写的推理逻辑连接感知、预测、规划、行为和运动等任务。我们提出了图式结构的问答对连接任务（GVQA），并为此新型任务提供了DriveLM-Data数据集。DriveLM-Data数据集包含两个独立的部分：DriveLM-nuScenes和DriveLM-CARLA。在DriveLM-nuScenes的情况下，我们基于当前主流方法构建了我们的数据集...欲了解更多详细信息，请访问数据集页面：https://huggingface.co/datasets/OpenDriveLab/DriveLM。

搜集汇总

背景与挑战

背景概述

DriveLM是一个用于自动驾驶的图视觉问答（GVQA）数据集，旨在通过人类编写的推理逻辑连接感知、预测、规划、行为和运动等多任务。该数据集包含两个部分：DriveLM-nuScenes基于真实世界nuScenes数据集构建，DriveLM-CARLA基于CARLA模拟器收集，提供问答对以支持自动驾驶系统的研究和开发。