As the development of autonomous vehicles progresses, efficient safety assurance methods become increasingly necessary. Safety assurance methods such as monitoring and scenario-based testing call for formalisation of driving scenarios. In this paper, we develop a temporal-logic formalisation of an important class of critical scenarios in the ISO standard 34502. We use signal temporal logic (STL) as a logical formalism. Our formalisation has two main features: 1) modular composition of logical formulas for systematic and comprehensive formalisation (following the compositional methodology of ISO 34502); 2) use of the RSS distance for defining danger. We find our formalisation comes with few parameters to tune thanks to the RSS distance. We experimentally evaluated our formalisation; using its results, we discuss the validity of our formalisation and its stability with respect to the choice of some parameter values.

Despite large advances in recent years, real-time capable motion planning for autonomous road vehicles remains a huge challenge. In this work, we present a decision module that is based on set-based reachability analysis: First, we identify all possible driving corridors by computing the reachable set for the longitudinal position of the vehicle along the lanelets of the road network, where lane changes are modeled as discrete events. Next, we select the best driving corridor based on a cost function that penalizes lane changes and deviations from a desired velocity profile. Finally, we generate a reference trajectory inside the selected driving corridor, which can be used to guide or warm start low-level trajectory planners. For the numerical evaluation we combine our decision module with a motion-primitive-based and an optimization-based planner and evaluate the performance on 2000 challenging CommonRoad traffic scenarios as well in the realistic CARLA simulator. The results demonstrate that our decision module is real-time capable and yields significant speed-ups compared to executing a motion planner standalone without a decision module.

Heterogeneous graphs offer powerful data representations for traffic, given their ability to model the complex interaction effects among a varying number of traffic participants and the underlying road infrastructure. With the recent advent of graph neural networks (GNNs) as the accompanying deep learning framework, the graph structure can be efficiently leveraged for various machine learning applications such as trajectory prediction. As a first of its kind, our proposed Python framework offers an easy-to-use and fully customizable data processing pipeline to extract standardized graph datasets from traffic scenarios. Providing a platform for GNN-based autonomous driving research, it improves comparability between approaches and allows researchers to focus on model implementation instead of dataset curation.