GPU-Accelerated Real-Time Path Planning and the Predictable Execution Model

Path planning is one of the key functional blocks for autonomous vehicles constantly updating their route in real-time. Heterogeneous many-cores are appealing candidates for its execution, but the high degree of resource sharing results in very unpredictable timing behavior. The predictable execution model (PREM) has the potential to enable the deployment of real-time applications on top of commercial off-the-shelf (COTS) heterogeneous systems by separating compute and memory operations, and scheduling the latter in an interference-free manner. This paper studies PREM applied to a state-of-the-art path planner running on a NVIDIA Tegra X1, providing insight on memory sharing and its impact on performance and predictability. The results show that PREM reduces the execution time variance to near-zero, providing a 3× decrease in the worst case execution time.