Developing Explainable Approximating Representation for Intelligent Transportation Systems

Improving trust in the operation of intelligent transportation systems (ITSs) is an actual challenge for overcoming the trough of disillusionment in the development phase regarding autonomous vehicles (AVs). There are identified critical gaps in the field that motivate the development of new theoretically grounded methods: most of the existing methods can be used for specific systems without generality in applicability, and the achieved explainability level is aimed only engineers and specialists. This paper aims to provide a method for developing an explainable representation on a specific ITS, such as intersection management with AV. The challenge is to find a transformation method which the explainable representation is resulted in. In this paper a decision-tree-based solution is proposed that results in a low-order approximating system in explainable form. It is presented an optimization method that results in the decision tree through the selection of its parameters, focusing on the selected ITS problem. The achieved rules within the explainable representation are used for supporting the human driving strategy in order to reduce critical interactions between AVs and human-driven vehicles. The effectiveness of the method is illustrated through high number of simulation scenarios. The outcome of the simulation is that the number of critical and risky interactions can be significantly reduced, if the rules from the explainable representation are considered.