Designing Middleware for Ethical Decision-Making(EDM) in Autonomous Driving: Bias Detection Algorithms(BDA) for Enhanced Pedestrian Situation Awareness

As autonomous driving technology develops, the importance of system safety and ethical decision-making is increasingly emphasized. In this paper, we propose a new scheduling framework for Bias Detection Algorithm(BDA) designed to improve pedestrian situation awareness in autonomous vehicles. The focus is on developing algorithms that eliminate prejudice against various pedestrians and ensure fairness and responsiveness in awareness. By analyzing scenarios and designing target algorithms, we aim for a fair and safe system by improving response to unpredictable situations. Leveraging parallel processing and distributed computing, this framework ensures timely and fair decisions in complex urban environments. It is emphasized that the capabilities of the proposed algorithm should be applied in various pedestrian scenarios to significantly improve safety and system efficiency.