In order to improve road safety, recent studies suggest that it is important to study and identify the optimal driving benchmarks that reflect the safest driving behaviour that may be observed by human drivers. The objective of this paper is to identify boundaries of risky and typical driving by studying the car-following driving behaviour. The data used in this study was collected by TNO in a recent naturalistic driving study. The distributions of driving metrics related to the following and leading vehicle were illustrated to understand their shapes and outliers. The safety-related car-following driving metrics of Time to Collision (TTC), Deceleration Rate to Avoid the Crash (DRAC), Crash Index (CI) and over-speeding were calculated, with risky thresholds obtained from the literature, and typical driving thresholds based expert assessors’ ratings. Principal Component Analysis (PCA) was applied to these metrics and showed that ‘optimal driving’ can be represented by one linear component that represents over 95% of the total dataset’s variance.

This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a definition of the two scientific fields in terms of safety, and identifies the most efficient approaches used regarding methodology, data collection and driving metrics. Results show that K-means and Neural Networks are the most commonly used methodologies for driver profile identification, and Dynamic Time Warping for driving pattern detection. Most studies discovered driver profiles related to aggressiveness, considering mainly speed and acceleration as driving metrics. Based on the gaps and challenges identified, this paper provides a new framework for combining microscopic and macroscopic driving behavior analysis, instead of examining them separately as is the state-of-theart. Such combined results can potentially improve the development of traffic risk models, which could be exploited in applications that monitor drivers in real-time and provide feedback. These models will represent human behavior more accurately, which can eventually lead to the recognition of 'optimal' human driving patterns that Automated Vehicles (AV) could 'mimic' to become safer.

Road safety research is largely focused on prediction and prevention of technical, human or organisational failures that may result in critical conflicts or crashes. Indicators of traffic risk aim to capture the passage to unsafe states. However, research in other industries has shown that it is meaningful to analyse safety along the whole spectrum of behaviours. Knowing the causes and patterns of “successful” interactions, rather than failures, could give new insights on the complexity of the system and the adaptability and resilience of its users in handling the inherent risks. The concept is known as Safety-II and has been extensively explored in the aviation, healthcare and process engineering domains. In this paper, we explore a new Safety-II paradigm for road safety research. We briefly review Safety-II applications in other sectors. We then present a Safety-II model for road safety, by means of an inverse version of Hyden's “safety pyramid”. Furthermore, we discuss a number of key road safety goals, theories, analysis methods and data sources and map them into a tentative taxonomy of Safety-I and Safety-II applications. It is concluded that there can be opportunities and benefits from adopting this new mindset, in order to complement existing approaches.