Scenario Identification and Classification to Support the Assessment of Advanced Driver Assistance Systems

Abstract

In recent years, driver assistance systems in cars, buses, and trucks have become more common and powerful. In particular, the introduction of AI methods to sensors, signal fusion, and traffic recognition allows us to step forward from actual level-2 assistance to level-3 Advanced Driver Assistance Systems (ADAS), where driving becomes autonomous and responsibility shifts from the driver to the automobile manufacturers. This, however, requires a high-precision risk assessment of failure, which can only be achieved by extensive data acquisition and statistical analysis of real traffic scenarios (which is impossible to perform by humans). Therefore, critical driving situations have to be identified and classified automatically. This paper develops and compares two different strategies—a traditional rule-based approach derived from deterministic causal considerations, and an AI-based approach trained with idealized cut-in, cut-out, and cut-through maneuvers. Application to a 10-h measurement sequence on a German highway demonstrates that the latter has the higher performance, whereas the former misses some of the safety-relevant events to be identified.