3D graphical functions in cars enjoy growing popularity. For instance, analog instruments of the instrument cluster are replaced by digital 3D displays as shown by Mercedes-Benz in the F125 prototype car. The trend to use 3D applications expands into two directions: towards more safety-relevant applications such as the speedometer and towards third-party applications, e.g., from an app store. Traditionally, to ensure isolation, new automotive functions are often implemented by adding further electronic control units (ECUs). However, in order to save cost, energy, and installation space, all 3D applications should share a single hardware platform and thus a single GPU. GPU sharing brings up the problem of providing real-time guarantees for rendering content of time-sensitive applications like the speedometer. This requires effective real-time GPU scheduling concepts to ensure safety and isolation for 3D rendering. Since current GPUs are not preemptive, a deadline-based scheduler must know the GPU execution time of GPU commands in advance. Unfortunately, existing scheduling concepts lack support for dynamic tasks, periodic real-time deadlines, or non-preemptive execution.

In this work, we present the requirements that apply to automotive HMI rendering. Based on these requirements, we propose a Virtualized Automotive Graphics System (VAGS), which uses a hypervisor providing isolation between different VMs, in particular for the head unit and for the instrument cluster.

Additionally, we present a novel framework to measure and predict the execution time of GPU commands using OpenGL ES 2.0. We propose prediction models for the GPU commands relevant for 3D rendering such as Draw and SwapBuffers. For Draw we present two heuristics to estimate the number of fragments, two concepts to estimate the shader execution time, and an optional online adaption mechanism. The number of fragments is estimated either by the bounding box of the rendered model, on which the vertex shader projection is applied, or by a subset of the triangles that is used to estimate the average size of a triangle. To estimate the shader execution time, we either execute them in a profiling environment with a dedicated OpenGL ES 2.0 Context, or we use a MARS (multivariate adaptive regression splines) model trained offline. The implementation and evaluation of our framework demonstrates its feasibility and shows that good prediction accuracy can be achieved. For instance, when rendering a popular 3D benchmark scene, less than 0.4 % of the samples were underestimated by more than 100 µs and less than 0.2 % of the samples were overestimated more than 100 µs. The overhead introduced by our prediction is negligible on some scenarios and typically below 25 % on the long-run. The application's initial startup is delayed by only about 30 ms of CPU time when using the most efficient concept.

Moreover, we present a real-time 3D GPU scheduling framework that provides strong guarantees for critical applications while still giving as much GPU resources to less important applications as possible, thus ensuring a high GPU utilization. The proposed concepts for execution time prediction are used to make good scheduling decisions and are required since current GPUs are not preemptive. Our implementation is based on an automotive embedded system running Linux and our evaluations show the feasibility and effectiveness of our concepts. The GPU scheduler fulfills given real-time constraints for a dynamic set of applications submitting arbitrary sequences of GPU command batches. It achieves a high GPU utilization of 99 % in a challenging scenario with 17 applications and fulfills 99.9 % of the deadlines of the highest-priority application. Moreover, scheduling is performed highly efficient in real-time with less than 9 µs latency.