Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations

In recent years, autonomous vehicles (AVs) have gained significant attention due to their potential to reduce traffic fatalities. The widespread adoption of AV technology is contingent not only on technical performance but also on public trust, with concerns centering on safety and potential technological malfunctions [1, 2]. A key factor in improving trust in autonomous systems is the ability to understand and replicate human driving behavior. However, worldwide, road accidents cause over 1.19 million deaths annually, with a majority resulting from human error [3], hence following human driving pattern is not always desired. Since the majority of accidents are caused by human error, analyzing human driving data allows us to identify common mistakes and undesirable driving patterns. This understanding is crucial for training machine learning models, such as those used in behavior cloning, where the goal is to mimic human driving behavior. Identifying undesirable driving patterns is especially useful for achieving a defensive driving behavior, which is proven to play a significant role in increasing passenger comfort and trust in AVs [4].