An Advanced Driver Assistance Test Cases Generation Methodology Based on Highway Traffic Situation Description Ontologies

Abstract

Autonomous cars mainly rely on an intelligent system pilot to achieve the purpose of self-driving. They combine a variety of sensors to perceive their surroundings, such as cameras, radars and lidars. The perception algorithms of the Advanced Driver-Assistance Systems (ADAS) provide observations on the environmental elements based on the data provided by the sensors, while decision algorithms generate the actions to be implemented by these vehicles. To ensure the safety of the autonomous vehicle, it is necessary to specify, validate and secure the dependability of the architecture and the behavioural logic of ADAS running on the vehicle for all the situations that will be met by the vehicle. These situations are described and generated as different test cases. In this work, we propose a methodology to generate automatically test cases of autonomous vehicle for highway. This methodology is based on a three layers hierarchy. The first layer exploits static and mobile concepts we have defined in the context of three ontologies: highway, weather and vehicle. The second layer exploits the relationships between these concepts while the third one exploits the method of test case generation based on the first two layers. Finally, we use the Performance Evaluation Process Algebra (PEPA) for modelling the transitions between the driving scenes. To apply our methodology, we consider a running example about a riding vehicle on the left of the autonomous vehicle to take a right exit lane of a highway.

Supported by IRT SystemX.