This paper presents the challenges that researchers must overcome in traffic light
recognition (TLR) research and provides an overview of ongoing work. The aim is to …

Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current
transportation systems, Traffic Light Detection (TLD) is still considered an important module …

Traffic light detection (TLD) is a vital part of both intelligent vehicles and driving assistance
systems (DAS). hard to determine the exact performance of a given method. In this paper we …

Image recognition is susceptible to interference from the external environment. It is
challenging to accurately and reliably recognize traffic lights in all-time and all-weather …

On the road to making self-driving cars a reality, academic and industrial researchers are
working hard to continue to increase safety while meeting technical and regulatory …

Traffic light and sign detectors on autonomous cars are integral for road scene perception.
The literature is abundant with deep learning networks that detect either lights or signs, not …

Recent work done on traffic sign and traffic light detection focus on improving detection
accuracy in complex scenarios, yet many fail to deliver real-time performance, specifically …

Traffic light recognition is being intensively researched for the purpose of reducing traffic
accidents at intersections and realizing autonomous driving. However, conventional vision …

Detection of the traffic light is a key function of the automatic driving system for urban traffic.
Considering the characteristics of classical and self‐learning algorithms, a fusion logic is …

Deep neural networks come as an effective solution to many problems associated with
autonomous driving. By providing real image samples with traffic context to the network, the …