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.

Already today, car manufacturers are designing E/E architectures using so-called component platforms. Such a platform comprises the superset of all components that are required to build all acquirable variants of a certain or even multiple car models. To find and optimize such component platforms, each candidate platform has to be evaluated by (a) determining a number of design objectives (monetary cost, etc.) of each car variant when derived from the candidate platform and then (b) approximating the platform's design objectives themselves, e. g., by a weighted sum that includes the expected sales of each variant. But typically, since this optimization has to take place in early design stages, important parameters like the number of expected sales numbers per car variant can only be projected and are, thus, uncertain. To investigate the susceptibility of the optimization to such uncertain parameters, this paper proposes a Monte-Carlo simulation-based method that enables to evaluate the uncertainty of a combined multi-variant objective wrt. parameter variations. By treating the minimization of uncertainty as an additional design objective, not only can the robustness of the derived component platforms be improved but also the confidence of the manufacturer. Moreover, we also propose to treat uncertainty not as a conventional design objective, but to use uncertain objectives: Here, not a single (e. g., mean) value but an interval given by observed upper and lower objective values is used. Experimental results show that the design objectives of an E/E architecture component platform are relatively robust wrt. parameter variations (here expected sales numbers of car variants). Moreover, it will be shown that the difference in expected overall costs between different non-dominated solutions is often much higher than the expected variation in cost as a result of parameter uncertainty