Interpretable End-to-End Driving Model for Implicit Scene Understanding
Yiyang Sun Xiaonian Wang Yangyang Zhang Jiagui Tang Xiaqiang Tang Jing Yao
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.