According to the World Health Organization, more than 1.25 million people die every year due to road accidents. Injuries caused by road accidents lead to significant economic losses for individuals, their families and nations as a whole. In most cases, these accidents are caused by the negligence of the driver. To reduce this accident rate, researchers in the field of automotive industry are eager to solve this problem. One of the most innovative approaches is the Advanced Driver Assistance System (ADAS), which seeks to eliminate driver negligence by introducing several 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 useful in achieving this. The collision avoidance system also proves to be a valuable asset for the automotive industry as it can act promptly if the driver does not react. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essayADAS is based on radar and camera sensors. The radar has proven useful in various weather conditions. Most automotive radars are based on frequency modulated continuous wave (FMCW). The main reason for this is that 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 they have the disadvantage of high data transmission rates. To reduce the amount of data sampled, you can omit random samples. 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 for real-world scenarios. The objective of the thesis is to solve this problem and evaluate these compressed sensing techniques for automotive radars and analyze the influence of different parameters on the reconstruction result. Furthermore, the goal is to reconstruct the signal with minimum cost. This can be achieved with the help of comparing different reconstruction algorithms based on quality measures. In addition to compressed sensing, interference problems can also occur between signals that cause degradation of the signal-to-noise ratio at the receiving end and, therefore, place severe limitations on the radar's sensing capabilities. For this reason, the probability of detecting weak targets is reduced due to missing information. The red car has a radar mounted on the bumper. It receives the echo of the green car, but at the same time it also receives the signal of the yellow car. This will create noise in the signal received by the red car and thus cause interference and missing data. There are several interference cancellation techniques to overcome this problem. But it also has disadvantages. Even eliminating the interference, it was not possible to recover the information from that interfered part. Therefore, this missing data needs to be recovered with the help of reconstruction algorithms.