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.

Surrogate Safety Measures (SSMs) are used to express road safety in terms of the safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future evolution of traffic participant trajectories to generate a measure of risk, restricting their applicability to scenarios where these assumptions are valid. In response to this limitation, we present the novel Probabilistic RISk Measure derivAtion (PRISMA) method. The objective of the PRISMA method is to derive SSMs that can be used to calculate in real time the probability of a specific event (e.g., a crash). The PRISMA method adopts a data-driven approach to predict the possible future traffic participant trajectories, thereby reducing the reliance on specific assumptions regarding these trajectories. Since the PRISMA is not bound to specific assumptions, the PRISMA method offers the ability to derive multiple SSMs for various scenarios. The occurrence probability of the specified event is based on simulations and combined with a regression model, this enables our derived SSMs to make real-time risk estimations. To illustrate the PRISMA method, an SSM is derived for risk evaluation during longitudinal traffic interactions. Since there is no known method to objectively estimate risk from first principles, i.e., there is no known risk ground truth, it is very difficult, if not impossible, to objectively compare the relative merits of two SSMs. Instead, we provide a method for benchmarking our derived SSM with respect to expected risk trends. The application of the benchmarking illustrates that the SSM matches the expected risk trends. Whereas the derived SSM shows the potential of the PRISMA method, future work involves applying the approach for other types of traffic conflicts, such as lateral traffic conflicts or interactions with vulnerable road users.