Cost-effective ADAS for inter-vehicle distance estimation using computer vision and deep learning

Abstract

Road safety is a critical global issue, with traffic accidents causing substantial human and economic losses annually. Advanced Driver Assistance Systems (ADAS) have emerged as an effective solution, leveraging technologies such as computer vision and artificial intelligence to enhance driving safety. However, the high costs and complexity of these systems limit their widespread adoption. This paper presents a cost-effective ADAS prototype utilizing vision-based and deep learning technologies to monitor and maintain safe following distances between vehicles. The performance of the system is evaluated using standard datasets and real-world scenarios, demonstrating adaptability across diverse driving conditions. Additionally, the feasibility of deploying the system on low-resource devices, such as Raspberry Pi and Radxa Zero, is analyzed, highlighting its potential for practical applications.