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In this paper, we compare the performance of a genetic algorithm for test parameter optimization with simulated annealing and random testing. Simulated annealing and genetic algorithm both represent search-based testing strategies. In the context of autonomous and automated driving, we apply these methods to iteratively optimize test parameters, to aim at obtaining critical scenarios that form the basis for virtual verication and validation of Advanced Driver Assistant System (ADAS). We consider a test scenario to be critical if the underlying parameter set causes a malfunction of the system equipped with the ADAS function (i.e., near-crash or crash of the vehicle). To assess the criticality of each test scenario we rely on time-to-collision (TTC), which is a well-known and often used time-based safety indicator for recognizing rear-end conicts. For evaluating the performance of each testing strategy, we set up a simulation framework, where we automatically run simulations for each approach until a predened minimal TTC threshold is reached or a maximal number of iterations has passed. The genetic algorithm-based approach showed the best performance by generating critical scenarios with the lowest number of required test executions, compared to random testing and simulated annealing. Keywords: Autonomous vehicles • Genetic algorithm • Simulated annealing • System verication • Automatic testing.

Having systems that can adapt themselves in case of faults or changing environmental conditions is of growing interest for industry and especially for the automotive industry considering autonomous driving. In autonomous driving, it is vital to have a system that is able to cope with faults in order to enable the system to reach a safe state. In this paper, we present an adaptive control method that can be used for this purpose. The method selects alternative actions so that given goal states can be reached, providing the availability of a certain degree of redundancy. The action selection is based on weight models that are adapted over time, capturing the success rate of certain actions. Besides the method, we present a Java implementation and its validation based on two case studies motivated by the requirements of the autonomous driving domain. We show that the presented approach is applicable both in case of environmental changes but also in case of faults occurring during operation. In the latter case, the methods provide an adaptive behavior very much close to the optimal selection.

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