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Millimeter-wave automotive radars can prevent traffic accidents and save human lives as they can detect vehicles and pedestrians even in night and in bad weather. Various types of automotive radars operating at 24 and 77 GHz bands are developed for various applications, like adaptive cruise control, blind-spot detection and lane change assistance. In each year, millions of millimeter-wave radar are sold worldwide. Millimeter-wave radar is composed of radar hardware and radar signal processing software, which detects the targets among noise, measures the distance, longitudinal speed and the azimuth angle of the targets, tracks the targets continuously, and controls the ego vehicle to brake or accelerate. Performance of the radar signal processing software is closely related with the radar hardware properties and radar measurement conditions.

Millimeter wave (MMW) automotive radar plays an important role in the advanced driving assistance system (ADAS), which detects vehicles, pedestrians and other obstacles. In the adaptive cruise control (ACC) and the automatic emergency brake (AEB) system, the target needs to be oriented. One of the automotive radar’s task is to get the direction information which includes the range, speed, azimuth and height of the target by high intermediate frequency (IF) signal sampling rate. In order to solve the problem of high sampling rate for the MMW radar caused by the traditional Nyquist sampling theorem when the target is located, a new method based on the compressed sensing (CS) for the target location is proposed in this paper. This paper presents the linear frequency modulated continuous wave (LFMCW) model and simulates the sampling and reconstruction of the radar’s IF signal via CS technique by using MATLAB.

Validation is one of the main challenges in development of automated driving systems (ADS). Due to the complexity of these systems and the various influence factors on their functional safety, current testcase generation methods can hardly guarantee the completeness and effectivity of the validation on system level. Separate validation of system components is a way to make system approval possible. In this paper, an approach is presented to generate deductively testcases for the validation of the environment perception sensors, which are the most essential components of ADS. This approach is originated from the model-based testing method, which is commonly used to validate software-based systems and extended by considering various external influence factors as follows: By modeling and analyzing applications in ADS, application oriented usecases of perception sensors are first derived.

Automotive radar is the most important component in the autonomous driving system, which detects the obstacles, vehicles and pedestrians around with acceptable cost. The target tracking is one of the key functions in the automotive radar which estimates the position and speed of the targets having regarding to the measurement inaccuracy and interferences. Modern automotive radar requires a multi-target tracking algorithm, as in the radar field of view hundreds of targets can present. In practice, the automotive radar faces very complicated and fast-changing road conditions, for example tunnels and curved roads. The targets’ unpredictable movements and the reflections of the electromagnetic wave from the tunnel walls and the roads will make the multi-target tracking a difficult task. Such situation may last several seconds so that the continuous tracks of the targets cannot be maintained and the tracks are dropped mistakenly.