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An alternative to the current drum brakes, with the increased requirements of todays daily service are disc brakes, in that they offer, in contrast to the drum brakes, the following technical advantages and in turn enhance the active safety of modern commercial vehicles when braking: Enhanced brake pedal-feedback and actuation Improved efficiency Little performance losses when high thermal loads occur (fading). In order to be able to determine the improvement potential of disc brakes they will be compared to the commonly employed Simplex drum brakes. Both wheel brake systems (disc-/drum brakes and all variations) were tested on a computer controlled brake dynamometer and in field tests using a heavy duty commercial vehicle (class 8). The results are compared and conclusions drawn regarding “advantages/disadvantages”.

In the automotive embedded system domain, the measurements from vehicle and Hardware-In-Loop are currently evaluated against the testcases, either manually or via automation scripts. These evaluations are localized; they evaluate a limited number of signals for a particular measurement without considering system-level behavior. This results in defect leakage. This study aims to develop a tool that can notify anomalies at the signal level in a new measurement without referring to the testcases, considering a more significant number of system-level signals, thereby significantly reducing the defect leakage. The tool learns important features and patterns of each maneuver from many historical measurements using deep learning techniques. We tried two CNN (convolution neural network) models. The first one is a specially designed CNN that does this maneuver classification and class-specific feature extraction.