Abstract Automated driving functions are among the most critical software components to develop. Before deployment in series vehicles, it has to be shown that the functions drive safely and in compliance with traffic rules. Despite the coverage that can be reached with very large amounts of test drives, corner cases remain possible. Furthermore, the development is subject to time-to-delivery constraints due to the highly competitive market, and potential logical errors must be found as early as possible. We describe an approach to improve the development of an actual industrial behavior planner for the Automated Driving Alliance between Bosch and Cariad. The original process landscape for verification and validation is extended with model checking techniques. The idea is to integrate automated extraction mechanisms that, starting from the C++ code of the planner, generate a higher-level model of the underlying logic. This model, composed in closed loop with expressive environment descriptions, can be exhaustively analyzed with model checking. This results, in case of violations, in traces that can be re-executed in system simulators to guide the search for errors. The approach was exemplarily deployed in series development, and successfully found relevant issues in intermediate versions of the planner at development time.

MOTIVATION: Alignment of multiple liquid chromatography/mass spectrometry (LC/MS) experiments is a necessity today, which arises from the need for biological and technical repeats. Due to limits in sampling frequency and poor reproducibility of retention times, current LC systems suffer from missing observations and non-linear distortions of the retention times across runs. Existing approaches for peak correspondence estimation focus almost exclusively on solving the pairwise alignment problem, yielding straightforward but suboptimal results for multiple alignment problems. RESULTS: We propose SIMA, a novel automated procedure for alignment of peak lists from multiple LC/MS runs. SIMA combines hierarchical pairwise correspondence estimation with simultaneous alignment and global retention time correction. It employs a tailored multidimensional kernel function and a procedure based on maximum likelihood estimation to find the retention time distortion function that best fits the observed data. SIMA does not require a dedicated reference spectrum, is robust with regard to outliers, needs only two intuitive parameters and naturally incorporates incomplete correspondence information. In a comparison with seven alternative methods on four different datasets, we show that SIMA yields competitive and superior performance on real-world data. AVAILABILITY: A C++ implementation of the SIMA algorithm is available from http://hci.iwr.uni-heidelberg.de/MIP/Software.