DISTRIBUTED DEEP LEARNING FOR REAL-TIME OBJECT DETECTION IN AUTONOMOUS VEHICLES: A PERFORMANCE OPTIMIZATION STUDY
Author(s): Mr. Ronak Goyal, Mrs. Ashwini Somani, Ms. Naveena Rukumani Kannan
Publication #: 2603020
Date of Publication: 30.03.2026
Country: India
Pages: 1-10
Published In: Volume 12 Issue 2 March-2026
Abstract
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 enhance RTD performance, while MC negatively affects it; CU showed no significant influence. The findings highlight the critical role of scalable computation and efficient data flow in optimizing real-time AI-based object detection. This study offers valuable insights for AI engineers, system architects, and policymakers aiming to advance intelligent mobility infrastructure in high-density urban environments. It also presents a framework for future research in AI deployment across real-time applications.
Keywords:
Download/View Count: 8
Share this Article