The future of automated driving (AD) is rooted in the development of robust, fair and explainable artificial intelligence methods. Upon request, automated vehicles must be able to explain their decisions to the driver and the car passengers, to the pedestrians and other vulnerable road users and potentially to external auditors in case of accidents. However, nowadays, most explainable methods still rely on quantitative analysis of the AD scene representations captured by multiple sensors. This paper proposes a novel representation of AD scenes, called Qualitative eXplainable Graph (QXG), dedicated to qualitative spatiotemporal reasoning of long-term scenes. The construction of this graph exploits the recent Qualitative Constraint Acquisition paradigm. Our experimental results on NuScenes, an open real-world multi-modal dataset, show that the qualitative eXplainable graph of an AD scene composed of 40 frames can be computed in real-time and light in space storage which makes it a potentially interesting tool for improved and more trustworthy perception and control processes in AD.

翻译：自动驾驶（AD）的未来植根于发展稳健、公平且可解释的人工智能方法。应要求，自动驾驶车辆必须能够向驾驶员和车内乘客、行人及其他弱势道路使用者，以及在事故发生时可能向外部审计人员解释其决策。然而，目前大多数可解释方法仍依赖于对多传感器捕获的AD场景表征进行定量分析。本文提出一种新的AD场景表征方法，称为定性可解释图（QXG），专门用于长时间场景的定性时空推理。该图的构建利用了近期提出的定性约束获取范式。我们在真实世界开放多模态数据集NuScenes上的实验结果表明，由40帧组成的AD场景的定性可解释图可实时计算且存储空间占用小，这使其成为改进AD中更可信的感知与控制过程的潜在有效工具。