Optimizing embedded AI systems for autonomous driving: Challenges and solutions using bayesian networks

Abstract. Embedded Artificial Intelligence (AI) systems are important components of autonomous vehicles. However, incorporating AI into autonomous vehicles is technically complex, due to the constraints of computation, real-time processing of data, uncertainty handling, and hardware limitations. Bayesian Networks (BNs) are a promising method that allows probabilistic modelling in adaptive learning and environment perception. Here we report on an overview of the application of BNs on autonomous driving, with an emphasis on how BNs can be optimized for embedded system resource constraints, including both computational and energy. Various optimization techniques are discussed, such as model pruning, approximation, acceleration using hardware accelerators, such as Field-Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuit (ASICs), and advanced cooling and power management to ensure AI reliability under high computational load. By reviewing these approaches, we aim to contribute to the development of more robust and green AI systems for autonomous driving.