Adaptive Localization for Autonomous Racing Vehicles with Resource-Constrained Embedded Platforms

Modern autonomous vehicles have to cope with the consolidation of multiple critical software modules processing huge amounts of real-time data on power- and resource-constrained embedded MPSoCs. In such a highly-congested and dynamic scenario, it is extremely complex to ensure that all components meet their quality-of-service requirements (e.g., sensor frequencies, accuracy, responsiveness, reliability) under all possible working conditions and within tight power budgets. One promising solution consists of taking advantage of complementary resource usage patterns of software components by implementing dynamic resource provisioning. A key enabler of this paradigm consists of augmenting applications with dynamic reconfiguration capability, thus adaptively modulating quality-of-service based on resource availability or proactively demanding resources based just on the complexity of the input at hand. The goal of this paper is to explore the feasibility of such a dynamic model of computation for the critical localization function of self-driving vehicles, so that it can burden on system resources just for what is needed at any point in time or gracefully degrade accuracy in case of resource shortage. We validate our approach in a harsh scenario, by implementing it in the localization module of an autonomous racing vehicle. Experiments show that we can adapt to variations in operational conditions such as the system workload, and that we can also achieve an overall reduction of platform utilization and power consumption for this computation-greedy software module by up to
$1.6\times$
and
$1.5\times$
, respectively, for roughly the same quality of service.