DISTRIBUTED DEEP LEARNING FOR REAL-TIME OBJECT DETECTION IN AUTONOMOUS VEHICLES: A PERFORMANCE OPTIMIZATION STUDY

This study examines the impact of key computational variables—Degree of Parallelism (DP), Compute Utilization (CU), Bandwidth Throughput (BT), and Model Complexity (MC)—on Real-Time Detection (RTD) performance in distributed deep learning systems for autonomous vehicles. Using a structured questionnaire and a sample of 327 respondents from New York’s autonomous systems industry, the research applied multiple linear regression analysis via R Studio. Results revealed that DP and BT significantly e