Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash

TL;DR

This paper analyzes the 2018 Uber self-driving car crash using computer vision techniques to evaluate whether pedestrian safety could have been improved, highlighting the challenges and potential solutions for autonomous vehicle safety in low-light conditions.

Contribution

The study applies advanced computer vision models to a real-world crash case, assessing image enhancement and object recognition methods for pedestrian safety in low-light scenarios.

Findings

Certain computer vision techniques improved pedestrian detection in low-light conditions.

The analysis identified specific limitations of current models in real-world autonomous driving scenarios.

Recommendations for enhancing autonomous vehicle safety systems in challenging lighting conditions.

Abstract

Human lives are important. The decision to allow self-driving vehicles operate on our roads carries great weight. This has been a hot topic of debate between policy-makers, technologists and public safety institutions. The recent Uber Inc. self-driving car crash, resulting in the death of a pedestrian, has strengthened the argument that autonomous vehicle technology is still not ready for deployment on public roads. In this work, we analyze the Uber car crash and shed light on the question, "Could the Uber Car Crash have been avoided?". We apply state-of-the-art Computer Vision models to this highly practical scenario. More generally, our experimental results are an evaluation of various image enhancement and object recognition techniques for enabling pedestrian safety in low-lighting conditions using the Uber crash as a case study.