Perception is a fundamental component of autonomous and self-driving vehicles, with reliable object detection and understanding of the environment being critical for safe operation. While lidar and camera based systems are widely used, radar remains a promising option due to its robustness in poor weather conditions and ability to directly measure the radial velocity of objects via the Doppler effect. However, radar’s sparse data and resulting limitations have constrained its potential. This thesis investigates the use of dual automotive radar setups to mitigate these limitations, and use the specific advantages of such a setup to improve full velocity vector estimation methods. A novel algorithm is proposed to achieve more accurate velocity estimation in non-ideal, real world conditions. The work further explores how improved velocity estimation can be used to improve classification performance using graph neural networks. Here it was found that including velocity information via a ground truth method did increase classification performance significantly, though the same result could not be obtained via the previously mentioned velocity estimation method. To support evaluation, a simplified simulation environment and ground truth velocity data for the RadarScenes dataset are developed. This research aims to close the performance gap between radar and other more data-dense sensors, offering a robust and more cost effective alternative, especially in conditions where optical systems under perform.

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.