Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash
arXiv:1805.11815 · doi:10.1007/978-3-030-12388-8_19
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.
arXiv comment10 pages, 8 figures, 3 tables
References in corpus (9)
- SSD: Single Shot MultiBox Detector
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- DehazeNet: An End-to-End System for Single Image Haze Removal
- VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
- Deep Bilateral Learning for Real-Time Image Enhancement
- Fast R-CNN
- MSR-net:Low-light Image Enhancement Using Deep Convolutional Network
- Ten Years of Pedestrian Detection, What Have We Learned?
- LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
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