Cell-based update algorithm for occupancy grid maps and hybrid map for ADAS on embedded GPUs

Advanced Driver Assistance Systems (ADASs), such as autonomous driving, require the continuous computation and update of detailed environment maps. Today's standard processors in automotive Electronic Control Units (ECUs) struggle to provide enough computing power for those tasks. Here, new architectures, like Graphics Processing Units (GPUs) might be a promising accelerator candidate for ECUs. Current algorithms have to be adapted to these new architectures when possible, or new algorithms have to be designed to take advantage of these architectures. In this paper, we propose a novel parallel update algorithm, called cell-based update algorithm for occupancy grid maps, which exploits the highly parallel architecture of GPUs and overcomes the shortcomings of previous implementations based on the Bresenham algorithm on such architectures. A second contribution is a new hybrid map, which takes the advantages of the classic occupancy grid map and reduces the computational effort of those. All algorithms are parallelized and implemented on a discrete GPU as well as on an embedded GPU (Nvidia Tegra K1 Jetson board). Compared with the state-of-the-art Bresenham algorithm as used in the case of occupancy grid maps, our parallelized cell-based update algorithm and our proposed hybrid map approach achieve speedups of up to 2.5 and 4.5, respectively.