End-to-End Autonomous Driving Risk Analysis: A Behavioural Anomaly Detection Approach

Autonomous vehicles (AV) have advanced considerably over the past decade and their potential to reduce road accidents is without equal. That said, the evolution towards fully automated driving will be accompanied by new and unfamiliar risks. The deployment of AVs hinges on the premise that they are considerably safer than human drivers. However, the ability of manufacturers, insurers and regulators to quantifiably demonstrate this risk reduction, relative to humans, presents a major barrier. Based on accident rates, it will likely take hundreds of millions of autonomous miles to derive statistically meaningful results. This paper addresses this issue and proposes a novel means of quantifying AV accident risks by benchmarking against a more familiar and quantifiable risk - Human Behaviour. This method is used to proactively quantify AV safety relative to human drivers. Currently, anomalous driving behaviour stems from human susceptibilities such as fatigue or aggression. We exploit this observation and explore AV driving behaviour where driving anomalies are symptoms of technology errors. The comparative behaviours of AV and safe human driving can be used to measure AV accident risk. An end-to-end model AV is simulated using Convolutional Neural Networks (CNN) to compare human and AV driving behaviours. Using a machine learning technique called Gaussian Processes (GP), contextual driving anomalies are detected, the frequency and severity of which are used to derive a risk score. This paper offers a starting point for addressing the challenges surrounding AV risk modelling.