In the realm of autonomous driving, the detection and prediction of pedestrian movement poses at high speeds are crucial for enhancing vehicular safety. Traditional imaging technologies, while rich in detail, suffer from limitations such as low frame rates and shutter-induced latencies, which can impede the rapid detection necessary in high-speed environments. This paper introduces a novel algorithm that leverages the capabilities of Dynamic Vision Sensors (DVS) to detect pedestrian poses under high-speed conditions. Unlike conventional cameras, DVS operate on the principle of capturing changes in light intensity at each pixel, allowing for data generation with high temporal resolution and minimal latency. Our approach integrates this technology with a Neural Architecture Search (NAS) optimized version of the YOLO-NAS model, specifically adapted to process the unique event-based data produced by DVS. This integration not only enhances the detection capabilities but also significantly reduces the system's response time. Comparative experiments demonstrate that our DVS-based system achieves a mean Average Precision (mAP) of 85.4%. These results underscore the potential of event-based vision sensors in transforming pedestrian detection frameworks within autonomous driving systems, offering substantial improvements in both accuracy and speed.