Reliably identifying relevant and safety-critical traffic scenarios is a central challenge in developing automated driving systems (ADSs). The development of objective measures to detect the intuitively perceived level of criticality across various scenarios remains unresolved. The major challenge lies in the evaluation of all sensitive metrics of all participants in a scenario, which goes beyond assessing individual criticality metrics or providing only non-operational definitions. To address these challenges, a modular and practice-oriented framework that assesses traffic criticality is proposed. Vehicular and environmental data can be used to objectively measure individual critical elements of traffic, which are then aggregated into a comprehensive scenario assessment. The framework is illustrated by applying it (i) to two maneuvers discussing effects of single criticality metrics and (ii) to two widely used datasets featuring highway and intersecting traffic for a statistical analysis. The assessment framework can be applied in many scenarios, ranging from identifying safety critical scenarios in existing data as a fundament for early development of ADSs to detecting safety critical emergency maneuvers in live data to trigger a deployed ADS. Fundamental to this process is the suitable selection of metrics, thresholds for critical behavior, and the system’s sensitivity in aggregating partially critical scenarios.

Fatally injured vulnerable road users, especially pedestrians who collided with motorised vehicles, account for 31% of all recorded fatalities in urban traffic in the EU. Autonomous vehicles will improve this situation in the future, reducing the impact of the human factor in critical traffic situations. The development of autonomous driving functions requires simulation environments to train certain behaviours. Consequently, these simulations need well represented vulnerable road users. In this paper, an approach for accurate prediction of pedestrian behaviour at street crossings is presented. The suggested solution involves an agent-based model working with A* pathfinding and risk-based areas, validated by a drone dataset on German road crossings.

The growing variety of transportation options and increasing traffic congestion pose new challenges for road safety. As a result, there is an intensified focus on developing automated driving features and assistance systems aimed at minimizing accidents caused by human errors. The creation of these systems requires a substantial amount of testing kilometers, with estimates suggesting that around 2.1 billion kilometers would be necessary to ensure that each situation pertinent to the driving function is encountered at least once with a probability of 50%. This paper advances the microscopic simulation of traffic scenarios beyond linear patterns, utilizing the open-source environment openPASS. It addresses the research question of whether existing microscopic simulations are able to realistically represent non-linear traffic scenarios. A comprehensive toolchain integrates simulation with video recordings and laser scans. The study compares recorded traffic flow data with simulations at a T-junction, assessing the realism of vehicle models and trajectory representation. Three scenarios are analyzed, considering vehicles and pedestrians. The 3D geometry of the scene was captured with a laser scanner, enabling the mapping of recorded video data onto a geo-referenced environment. Object trajectories were extracted using an ’Regions with Convolutional Neural Networks features’ object detector. While openPASS simulated vehicle and pedestrian behaviors effectively, limitations in trajectory variability and reaction times were observed. These findings highlight the need for more realistic behavior models. This research emphasizes the necessity for improvements to accommodate complex driving behaviors and pedestrian dynamics.