"In order to test highly autonomous system, it is required to re-think how the automotive industry must validate and certify Advanced Driver Assistance Systes (ADAS) and Autonomous Driving (AD) systems," said Mihai Nica from AVL. "Therefore, AVL participates with TU Graz to develop a unique and highly efficient method and workflow based on simulation and test case generation for prove fulfillment of Safety Of The Intended Functionality (SOTIF), quality and system integrity requirements of the autonomous systems".
Together the project team is working on using ontologies to describe the environment of driverless cars. These ontologies are knowledge bases for the exchange of relevant information within a machine system. For example, interfaces, behavior and relationships of individual system units can communicate with each other. In the case of autonomous driving systems, these would be "decision making", "traffic description" or "autopilot".
The Graz researchers worked with basic detailed information about environments in driving scenarios and fed the knowledge bases with details about the construction of roads, intersections and the like, which AVL provided. From this, driving scenarios can be derived, by using AVL's test case generation algorithm, that test the behaviour of the automated driving systems in simulations.
The researchers have used two algorithms to convert these ontologies into input models for combinatorial testing that can subsequently be executed using simulation environments. "In initial experimental tests we have discovered serious weaknesses in automated driving functions. Without these automatically generated test scenarios, the vulnerabilities would not have been detected so quickly: nine out of 319 test cases investigated have led to accidents," said Wotawa.
For example, in one test scenario, a brake assistance system failed to detect two people coming from different directions at the same time and one of them was badly hit due to the initiated braking maneuver. "This means that with our method, you can find test scenarios that are difficult to test in reality and that you might not even be able to focus on," he added.
The control method adaptively compensates for internal errors in the software system by selecting alternative actions in such a way that predetermined target states can be achieved, while providing a certain degree of redundancy. The selection is based on weighting models that are adjusted over time and measure the success rate of specific actions already performed. In addition to the method, the researchers also present a Java implementation and its validation using two case studies motivated by the requirements of driverless cars.
The AutoDrive project is at autodrive-project.eu