The rapid development of active safety systems in the automotive industry and research in autonomous driving requires reliable, high-precision sensors that provide rich information about the surrounding environment and the behaviour of other road users. In practice, there is always some non-zero mounting misalignment, i.e., angular inaccuracy in a sensor’s mounting on a vehicle. It is essential to accurately estimate and compensate for this misalignment further programmatically (in software). In the case of radars, imprecise mounting may result in incorrect/inaccurate target information, problems with the tracking algorithm, or a decrease in the power reflected from the target. Sensor misalignment should be mitigated in two ways: through the correction of an inaccurate alignment angle via the estimated value of the misalignment angle or alerting other components of the system of potential sensor degradation if the misalignment is beyond the operational range. This work analyses misalignment’s influences on radar sensors and other system components. In the mathematically proven example of a vertically misaligned radar, pedestrian detectability dropped to one-third of the maximum range. In addition, mathematically derived heading estimation errors demonstrate the impact on data association in data fusion. The simulation results presented show that the angle of misalignment exponentially increases the risk of false track splitting. Additionally, the paper presents a comprehensive review of radar alignment techniques, mostly found in the patent literature, and implements a baseline algorithm, along with suggested key performance indicators (KPIs) to facilitate comparisons for other researchers.

The automotive industry, particularly Active Safety and Autonomous Driving, requires a vast amount of data collected during test drives for machine learning and system validation. The data’s integrity is necessary to ensure the correct operation of the algorithms. In a situation where the amount of collected material exceeds thousands of hours, it becomes necessary to use automatic verification methods. Data integrity can be ensured by comparing signals with estimations based on measurements independent of the verified value. It often turns out, however, that designing such an algorithm and its accurate calibration requires considerable effort. This work proposes using a neural network as an alternative to an algorithmic approach to reduce development time drastically. The applied solution allows the estimation of the vehicle’s speed and yaw rate based on the detections from a single radar scan with a neural network consisting of less than 50,000 parameters. The accuracy of the estimation is sufficient to detect anomalies in the data by comparing the measured and estimated values. Further, a neural network that allows the direct detection of anomalies in the input data is presented.