Evaluating Two Semantics for Falsification using an Autonomous Driving Example
Proceedings (editor), 2019

We consider the falsification of temporal logic properties as a method to test complex systems, such as autonomous systems. Since these systems are often safety-critical, it is important to assess whether they fulfill given specifications or not. An adaptive cruise controller for an autonomous car is considered where the closed-loop model has unknown parameters and an important problem is to find parameter combinations for which given specification are broken. We assume that the closed-loop system can be simulated with the known given parameters, no other information is available to the testing framework. The specification, such as, the ability to avoid collisions, is expressed using Signal Temporal Logic (STL). In general, systems consist of a large number of parameters, and it is not possible or feasible to explicitly enumerate all combinations of the parameters. Thus, an optimization-based approach is used to guide the search for parameters that might falsify the specification. However, a key challenge is how to select the objective function such that the falsification of the specification, if it can be falsified, can be falsified using as few simulations as possible. For falsification using optimization it is required to have a measure representing the distance to the falsification of the specification. The way the measure is defined results in different objective functions usedduringoptimization.Differentmeasureshavebeenproposed in the literature and in this paper the properties of the Max Semantics (MAX) and the Mean Alternative Robustness Value (MARV) semantics are discussed. After evaluating these two semantics on an adaptive cruise control example, we discuss their strengths and weaknesses to better understand the properties of the two semantics.

Max Semantics

Falsification

Testing

Autonomous Driving.

Mean Alternative Robustness Value

Editor

Zahra Ramezani

Chalmers, Electrical Engineering, Systems and control, Automation

Nicholas Smallbone

Chalmers, Computer Science and Engineering (Chalmers), Functional Programming

Martin Fabian

Chalmers, Electrical Engineering, Systems and control, Automation

Knut Åkesson

Chalmers, Electrical Engineering, Systems and control, Automation

IEEE International Conference on Industrial Informatics, INDIN’19 Industrial Applications of Artificial Intelligence
Helsinki-Espoo, Finland,

Systematic testing of cyber-physical systems (SyTeC)

Swedish Research Council (VR), 2017-01-01 -- 2022-12-31.

Roots

Basic sciences

Subject Categories

Control Engineering

Computer Science

Computer Systems

More information

Created

12/21/2019