Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An Inductive Logic Programming Approach

TL;DR

This paper introduces an ILP-based approach for traffic sign detection in autonomous vehicles that is more robust against adversarial attacks, explainable, and requires minimal training data, enhancing safety and trustworthiness.

Contribution

The paper presents a novel ILP-based method for traffic sign detection that improves robustness to adversarial attacks and offers explainability, unlike traditional DNN classifiers.

Findings

ILP approach correctly identifies all targeted stop signs under attack.

Method is robust against PR2 and AdvCam adversarial attacks.

Requires minimal training data for effective detection.

Abstract

Traffic sign detection is a critical task in the operation of Autonomous Vehicles (AV), as it ensures the safety of all road users. Current DNN-based sign classification systems rely on pixel-level features to detect traffic signs and can be susceptible to adversarial attacks. These attacks involve small, imperceptible changes to a sign that can cause traditional classifiers to misidentify the sign. We propose an Inductive Logic Programming (ILP) based approach for stop sign detection in AVs to address this issue. This method utilises high-level features of a sign, such as its shape, colour, and text, to detect categories of traffic signs. This approach is more robust against adversarial attacks, as it mimics human-like perception and is less susceptible to the limitations of current DNN classifiers. We consider two adversarial attacking methods to evaluate our approach: Robust Physical Perturbation (PR2) and Adversarial Camouflage (AdvCam). These attacks are able to deceive DNN classifiers, causing them to misidentify stop signs as other signs with high confidence. The results show that the proposed ILP-based technique is able to correctly identify all targeted stop signs, even in the presence of PR2 and ADvCam attacks. The proposed learning method is also efficient as it requires minimal training data. Moreover, it is fully explainable, making it possible to debug AVs.