Critical software systems require stringent testing to identify possible failure cases, which can be difficult to find using manual testing. In this study, we report our industrial experience in testing a realistic R&D flight control system using a heuristic based testing method. Our approach utilizes evolutionary strategies augmented with intrinsic motivation to yield a diverse range of test cases, each revealing different potential failure scenarios within the system. This diversity allows for a more comprehensive identification and understanding of the system’s vulnerabilities. We analyze the test cases found by evolution to identify the system’s weaknesses. The results of our study show that our approach can be used to improve the reliability and robustness of avionics systems by providing high-quality test cases in an efficient and cost-effective manner.

Accurate motion models are key to many tasks in the intelligent vehicle domain, but simple Linear Dynamics (e.g. Kalman filtering) do not exploit the spatio-temporal context of motion. We present a method to learn Switching Linear Dynamics of object tracks observed from within a driving vehicle. Each switching state captures object dynamics as a mean motion with variance, but also has an additional spatial distribution on where the dynamic is seen relative to the vehicle. Thus, both an object's previous movements and current location will make certain dynamics more probable for subsequent time steps. To train the model, we use Bayesian inference to sample parameters from the posterior, and jointly learn the required number of dynamics. Unlike Maximum Likelihood learning, inference is robust against overfitting and poor initialization. We demonstrate our approach on an ego-motion compensated track dataset of pedestrians, and illustrate how the switching dynamics can make more accurate path predictions than a mixture of linear dynamics for crossing pedestrians.