The European-funded HAL4SDV project aims to advance European solutions in software-defined vehicles by introducing a hardware abstraction layer positioned between executed software and execution units. HAL4SDV includes over 60 partners across 12 countries and receives funding within the Chips Joint Undertaking under Horizon Europe since April 2024 and is coordinated by TTTech Computertechnik. The proposed hardware abstraction layer includes safety-critical scheduling and platform deployment of software tasks, and is motivated by the requirement for abstracted hardware with unified interfaces in centralized automotive architectures.This work presents a correct-by-construction workflow which is developed by academic partners to schedule and deploy periodic software tasks onto diverse execution units. The workflow facilitates the execution of the same task stack on multiple unit architectures and consists of a task model and scheduling algorithm, which is followed by platform deployment for diverse hardware units, ensuring safe execution. In this multi-partner project, a bandwidth regulation unit for hardware accelerators and a RISC-V-based multicore system with tightly coupled memories are used as target platforms.A RoboRacer driving stack is chosen for evaluation, showing the viability of our workflow to schedule autonomous driving functions. To show generalization capability, synthetic task sets are additionally used to validate our deployment workflow.

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.