Automatic Generation of Road Geometries to Create Challenging Scenarios for Automated Vehicles Based on the Sensor Setup
This work addresses safety validation for automated vehicles by creating specific, sensor-based challenging scenarios, but it is incremental as it builds on existing path-planning methods for a focused domain.
The paper tackles the problem of efficiently identifying challenging scenarios for offline safety assessment of automated vehicles by generating road geometries that minimize the ego-vehicle's perception of approaching objects, resulting in optimal paths for critical highway scenarios.
For the offline safety assessment of automated vehicles, the most challenging and critical scenarios must be identified efficiently. Therefore, we present a new approach to define challenging scenarios based on a sensor setup model of the ego-vehicle. First, a static optimal approaching path of a road user to the ego-vehicle is calculated using an A* algorithm. We consider a poor perception of the road user by the automated vehicle as optimal, because we want to define scenarios that are as critical as possible. The path is then transferred to a dynamic scenario, where the trajectory of the road user and the road layout are determined. The result is an optimal road geometry, so that the ego-vehicle can perceive an approaching object as poorly as possible. The focus of our work is on the highway as the Operational Design Domain (ODD).