RT book part
T1 Cost-effective ADAS for inter-vehicle distance estimation using computer vision and deep learning
A1 Guerrero Contreras, Gabriel José
A1 Balderas Díaz, Sara
A1 Díaz Gómez, Alejandro
A1 Medina Bulo, María Inmaculada
A1 Dominguez Jiménez, Juan José
A2 Ingeniería Informática
K1 Advanced Driver Assistance Systems (ADAS)
K1 computer vision
K1 deep learning
K1 distance estimation
K1 real-time detection
AB 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.
PB IEEE Xplore
SN 979-8-3315-2358-9
SN 2472-7571
YR 2025
FD 2025-08-26
LK http://hdl.handle.net/10498/37656
UL http://hdl.handle.net/10498/37656
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 05-nov-2026