Energy-Efficient and Context-Aware Computing in Software-Defined Vehicles for Advanced Driver Assistance Systems (ADAS)

The rise of Software-Defined Vehicles (SDV) has rapidly advanced the development of Advanced Driver Assistance Systems (ADAS), Autonomous Vehicle (AV), and Battery Electric Vehicle (BEV) technology. While AVs need power to compute data from perception to controls, BEVs need the efficiency to optimize their electric driving range and stand out compared to traditional Internal Combustion Engine (ICE) vehicles. AVs possess certain shortcomings in the current world, but SAE Level 2+ (L2+) Automated Vehicles are the focus of all major Original Equipment Manufacturers (OEMs). The most common form of an SDV today is the amalgamation of AV and BEV technology on the same platform which is prominently available in most OEM’s lineups. As the compute and sensing architectures for L2+ automated vehicles lean towards a computationally expensive centralized design, it may hamper the most important purchasing factor of a BEV, the electric driving range.

This research asserts that the development of dynamic sensing and context-aware algorithms will allow a BEV to retain energy efficiency and the ADAS to maintain performance. Moreover, a decentralized computing architecture design will allow the system to utilize System-on-Module (SoM) boards that can process Artificial Intelligence (AI) algorithms at the edge. This will enable refined hardware acceleration using Edge-AI. The research will propose the use of a novel Software-in-the-Loop (SiL) simulation environment for a 2023 Cadillac LYRIQ provided by the EcoCAR EV Challenge competition. Future work will involve an in-depth evaluation and discussion of the simulation data. We will conclude that optimizing sensing and computation in an SDV platform will allow Automated and Electric Vehicles to prosper concurrently without impeding their technological progress.