T-norm Selection for Object Detection in Autonomous Driving with Logical Constraints

Thomas Eiter, Katsumi Inoue, Nelson Higuera Ruiz, Sota Moriyama

Keywords: Neurosymbolic, Computer Vision, Fuzzy Logic

TL;DR: We implement a framework for learning constraints in vision models and algorithms to support this. We showcase it in Autonomous Driving.

Abstract: Integrating logical constraints into object detection models for autonomous driving (AD) is a promising way to enhance their compliance with rules and thereby increase the safety of the system. T-norms have been utilized to calculate the constrained loss, i.e., the violations of logical constraints as losses. While prior works have statically selected a few t-norms, we conduct an extensive experimental study to identify the most effective choices, as suboptimal t-norms can lead to undesired model behavior. To this end, we present MOD-ECL, a neurosymbolic framework that implements a wide range of t-norms and applies them in an adaptive manner. It includes an algorithm that selects well-performing t-norms during training and a scheduler that regulates the impact of the constrained loss. We evaluate its effectiveness on the ROAD-R and ROAD-Waymo-R datasets for object detection in AD, using attached common-sense constraints. Our results show that careful selection of parameters is crucial for effective constrained loss behavior. Moreover, our framework not only reduces constraint violations but also, in some cases, improves detection performance. Additionally, our methods offer fine-grained control over the trade-off between accuracy and constraint violation.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 23576