Convoy tracking for ADAS on embedded GPUs

Future Advanced Driver Assistance Systems (ADAS) need to create an accurate model of the environment. Accordingly, an enormous amount of data has to be fused and processed. From this data, information such as the positions of the vehicles, has to be extracted out of the model, e.g., to create a convoy track. Common architectures used today, like single-core processors in automotive Electronic Control Units (ECUs), struggle to provide enough computing power for those tasks. Here, emerging embedded multi-core architectures are appealing such as embedded Graphics Processing Units (GPUs). In this paper, we present a novel parallelization of a convoy track detection algorithm. Moreover, in order to profit best from for embedded GPUs, special techniques such as Zero Copy are exploited to parallelize our application. As an experimental platform, an Nvidia Tegra K1 is used, which is also common in the automotive industry. For different scenarios, we illustrate the limitations of the system and algorithm. Yet, impressive speedups with respect to a single-core CPU solution of up to nine may be achieved using the proposed parallelization techniques in case of high traffic situations.