Applications and Challenges of Multi-Core Scheduling in Intelligent Automotive Systems

Recent advancements in computing and autonomous driving technologies have led to the integration of new functionalities into intelligent automotive, such as environmental perception, path planning, assisted driving, and entertainment services. This integration requires the rapid processing of critical tasks, including collision detection, emergency braking, lane keeping, and Vehicle-to-Everything (V2X) communication, exceeding past functional demands. Consequently, high-performance multi-core processors have emerged as the preferred hardware solution due to their superior processing speeds, energy efficiency, and parallel task execution capabilities. This paper explores various applications of multi core processors in intelligent automotive systems, systematically reviewing recent advancements in multi-core scheduling methods. It categorizes and analyzes approaches to task loading, task migration, efficiency improvement, safety assurance, communication, and resource contention in both general and automotive contexts. To objectively assess these methods, the paper establishes reference standards for evaluating scheduling methods and system architectures and provides analytical methods aligned with these standards. Finally, the paper discusses future challenges, considers trends in intelligent automotive systems and multi-core processors, and offers recommendations for future research in this field.