Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case

TL;DR

This paper introduces a method using Knowledge Compilation to enhance the explainability of automated traffic light controllers by translating their decision-making process into a structured, understandable form based on historical data.

Contribution

It applies Knowledge Compilation theory to create an interpretable model for traffic light control decisions, bridging the gap between black-box models and explainability in ITS.

Findings

Structured representation relates states with actions effectively

Method improves interpretability of traffic light decisions

Applicable to real-time traffic management scenarios

Abstract

Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controller's decision given the state of the system. For this, we use simulated historical state-action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads.