  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.
