Autonomous driving can bring revolutionary advancements in transportation, by making vehicles navigate and operate independently without human intervention. To achieve this, autonomous vehicles are equipped with sensors, including cameras, Light Detection and Ranging (LiDAR), and radar, which provide them with a comprehensive view of their surroundings. Frequency-Modulated Continuous Wave (FMCW) radar is the most popular radar sensor employed on autonomous vehicles (AVs), for its long working distance, simultaneous accurate measurements of range and radial velocity of the target, and its robustness for all weather conditions. However, there are still many problems to be solved for FMCW radar for detecting vulnerable road users (VRUs), such as pedestrians. The Constant False Alarm Rate (CFAR) detector plays a crucial role in FMCW radar signal processing, for its adaptive capability to estimate multiple potential targets versus variable clutter and noise backgrounds, and it is often the first step of processing in many automotive radar pipelines.
There have been several published methods for detecting pedestrians. However, most methods hardly consider public real-world radar datasets for their performance evaluation. Meanwhile, conventional CFAR used in autonomous driving, such as Cell averaging CFAR (CA-CFAR) and Ordered-statistic CFAR (OS-CFAR) are not specially designed for Doppler-spread targets (DSTs), while pedestrians are typical DSTs which shows Doppler extension in range-Doppler-map (RDM). This is due to the movement of the arms and legs of pedestrians that make them not only extended targets at mm-wave frequencies but also present a spread Doppler signature.
Therefore, in this thesis a proposed CFAR detector for DSTs enhances the probability of detection compared to two-dimensional (2D) CA-CFAR and two-dimensional (2D) OS-CFAR. The parametric study is conducted on CA-CFAR and OS-CFAR detectors. Additionally, the computation time is reduced dramatically.