Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous
  Driving: An Inductive Logic Programming Approach
Zahra Chaghazardi Saber Fallah Alireza Tamaddoni-Nezhad
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.