Driving scene understanding is to obtain comprehensive scene information
through the sensor data and provide a basis for downstream tasks, which is
indispensable for the safety of self-driving vehicles. Specific perception
tasks, such as object detection and scene graph generation, are commonly used.
However, the results of these tasks are only equivalent to the characterization
of sampling from high-dimensional scene features, which are not sufficient to
represent the scenario. In addition, the goal of perception tasks is
inconsistent with human driving that just focuses on what may affect the
ego-trajectory. Therefore, we propose an end-to-end Interpretable Implicit
Driving Scene Understanding (II-DSU) model to extract implicit high-dimensional
scene features as scene understanding results guided by a planning module and
to validate the plausibility of scene understanding using auxiliary perception
tasks for visualization. Experimental results on CARLA benchmarks show that our
approach achieves the new state-of-the-art and is able to obtain scene features
that embody richer scene information relevant to driving, enabling superior
performance of the downstream planning.

通过传感器数据获取综合场景信息，并为下游任务提供基础，对于自动驾驶车辆的安全至关重要，本文提出了一种端到端的可解释性隐式驾驶场景理解模型，通过规划模块引导提取隐式高维场景特征作为场景理解结果，并使用辅助感知任务进行可视化验证，实验证明我们的方法在 CARLA 基准测试上达到了最新的最高水平，并能够获取体现与驾驶相关的更丰富的场景信息，实现下游规划的卓越性能。