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

arXiv: https://arxiv.org/abs/2309.03215 · HTML: https://mlsanity.com/paper/2309.03215/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 30 Aug 2023.

## Builds on (most cited first, 14 of 14)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Fooling automated surveillance cameras: adversarial patches to attack person detection](https://mlsanity.com/paper/1904.08653/) (2019)
- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [Physical Adversarial Examples for Object Detectors](https://mlsanity.com/paper/1807.07769/) (2018)
- [On the Robustness of Semantic Segmentation Models to Adversarial Attacks](https://mlsanity.com/paper/1711.09856/) (2017)
- [Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles](https://mlsanity.com/paper/2003.08757/) (2020)
- [Universal Adversarial Perturbations Against Semantic Image Segmentation](https://mlsanity.com/paper/1704.05712/) (2017)
- [An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models](https://mlsanity.com/paper/2002.02175/) (2020)
- [Neurosymbolic Reinforcement Learning with Formally Verified Exploration](https://mlsanity.com/paper/2009.12612/) (2020)
- [Turning 30: New Ideas in Inductive Logic Programming](https://mlsanity.com/paper/2002.11002/) (2020)
- [Fooling a Real Car with Adversarial Traffic Signs](https://mlsanity.com/paper/1907.00374/) (2019)
- [Simple Physical Adversarial Examples against End-to-End Autonomous Driving Models](https://mlsanity.com/paper/1903.05157/) (2019)
- [Rolling Colors: Adversarial Laser Exploits against Traffic Light Recognition](https://mlsanity.com/paper/2204.02675/) (2022)

## Built on by (most cited first, 3 of 3)

- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [Real-Time Incremental Explanations for Object Detectors in Autonomous Driving](https://mlsanity.com/paper/2408.11963/) (2024)
- [Correcting Autonomous Driving Object Detection Misclassifications with Automated Commonsense Reasoning](https://mlsanity.com/paper/2601.04271/) (2026)

## Abstract (arXiv)

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.