According to the World Health Organization, more than 1.25 million people die every year at the following road accidents. Injuries caused by road traffic accidents cause considerable economic losses to individuals, their families and to nations as a whole. In most cases, these accidents are caused by driver negligence. In order to reduce this accident rate, researchers in the automotive industry want to solve this problem. One of the most innovative approaches is the Advanced Driver Assistance System (ADAS), which attempts to eliminate driver negligence by introducing different mechanisms. This can not only reduce the accident rate, but also ensure the safety of passengers. Emergency braking and blind spot detection are two of many other mechanisms that can be helpful in achieving this goal. The collision avoidance system also proves to be a valuable asset for the automotive industry since it can take quick action if the driver does not react. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essay. ADAS relies on radar and camera sensors. Radar has proven useful in different weather conditions. Most automotive radars are based on frequency modulated continuous wave (FMCW). The main reason behind this is that the FMCW radar can calculate the range and speed of multiple targets simultaneously with good resolution. For autonomous driving, high-resolution radar sensors are key components, but have the disadvantage of high data rates. To reduce the amount of data sampled, random samples can be omitted. To estimate the missing data, several compressed sensing reconstruction techniques are used to recover the information. The problem with these techniques is that they require a large number of iterations and therefore cannot be useful in real-world scenarios. The aim of the thesis is to solve this problem and evaluate these compressed sensing techniques for automobile radars and analyze the influence of different parameters on the reconstruction result. Furthermore, the objective is to reconstruct the signal with minimum cost. This can be achieved using the comparison between different reconstruction algorithms based on quality measures. In addition to compressed detection, an interference problem between signals can also arise and cause a degradation of the signal-to-noise ratio at reception and, therefore, significantly limit the detection capabilities of the radar. As a result, the probability of detecting weak targets is reduced. due to missing information. The red car has a radar mounted on the bumper. It receives an echo from the green car, but at the same time it also receives a signal from the yellow car. This will create a disturbance in the signal received from the red car and therefore lead to interference and missing data. There are various interference cancellation techniques to overcome this problem. But this also has disadvantages. While canceling the interference, he was unable to recover the information from that disturbed part. This missing data must therefore be recovered using reconstruction algorithms..