4D imaging automotive radars are high-resolution, long-range sensors that can scan both horizontally and vertically, identifying the distance, direction, and relative velocity of detected objects. With higher resolution than earlier radar sensors, 4D radars can detect many different reflection points from the same object, which by mapping them out, begins to resemble an image. As they become more advanced, these radars are increasingly trusted as the primary sensor for autonomous driving applications, and when used in conjunction with PNT signals, they can build and update a map of radar reflection points around the unknown surrounding area of the vehicle. This capability is crucial for autonomous systems, and in particular its interpretation in identifying the freespace where the vehicle can navigate to. 

While the integration of advanced sensors on vehicles has led to incremental safety improvements, there is a growing consensus that full vehicle autonomy offers the most promising path to significantly reduce the number of road fatalities. which currently stands at over 1.35 million per annum. This belief is rooted in the persistent role of human error (whether due to inattention, distraction, or negligence) as the leading cause of accidents. 

Various methods currently exist for calculating freespace, each with limitations such as shadowing, increased computational complexity, or reliance on different sensor types. This project therefore aims to develop a method for calculating freespace using only radar detection data, resulting in a more complete and accurate prediction. The novel algorithm employs concave hulls to define the boundaries of occupied areas within the scene and uses a proprietary method to stitch these hulls together, allowing for freespace prediction across the entire field of view. The algorithm will be tested on Provizio’s 4D imaging radar sensor, using camera data as a ground truth and evaluating performance with and without PNT data.