Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle
technology. Image based benchmark datasets have driven development in computer vision …

From the early stages of autonomous vehicle's development, traffic light detection/perception
system have been an important area of research for making collision safe self-driving …

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 …

We present a weakly-supervised approach to segmenting proposed drivable paths in
images with the goal of autonomous driving in complex urban environments. Using recorded …

Recent improvements in object detection are driven by the success of convolutional neural
networks (CNN). They are able to learn rich features outperforming hand-crafted features …

Autonomous terrestrial vehicles must be capable of perceiving traffic lights and recognizing
their current states to share the streets with human drivers. Most of the time, human drivers …

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 …

Traffic light recognition is an essential task for an advanced driving assistance system
(ADAS) as well as for autonomous vehicles. Recently, deep‐learning has become …

Traffic light recognition is one of the important tasks in the studies of intelligent transport
system. In this paper, a robust traffic light recognition model based on vision information is …

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 …