Traffic light mapping and detection

Abstract

Abstract-The outdoor perception problem is a major challenge for driver-assistance and autonomous vehicle systems. While these systems can often employ active sensors such as sonar, radar, and lidar to perceive their surroundings, the state of standard traffic lights can only be perceived visually. By using a prior map, a perception system can anticipate and predict the locations of traffic lights and improve detection of the light state. The prior map also encodes the control semantics of the individual lights. This paper presents methods for automatically mapping the three dimensional positions of traffic lights and robustly detecting traffic light state onboard cars with cameras. We have used these methods to map more than four thousand traffic lights, and to perform onboard traffic light detection for thousands of drives through intersections.

Why did the robot cross the road? — Learning from multi-modal sensor data for autonomous road crossing

“…For traffic light regulated intersections, state-of-the-art approaches vary between combining computer vision-based methods with prior scene information to improve detection accuracy while reducing the search space within the image [2,9,13,18].…”

Trafik Işığı Tespiti Yapan Bir Sürücü Güvenlik Destek Sistemi

“…Mark ve arkadaşları [3], SDS için öğrenme tabanlı bir sistem önermişlerdir. Nathaniel ve arkadaşı [4], önceden haritalanmış trafik ışıklarından ve farklı sensor bilgilerinden yararlanarak renkli parça analizi ile trafik ışıklarını tespit etmiştir. Bu çalışmada trafik ışığı tespitine yönelik bir SGDS önerilmiştir [5].…”

Traffic Light Detection at Night: Comparison of a Learning-Based Detector and Three Model-Based Detectors

“…One solution to removing type 3, 4 and 5 false positives could be the introduction of prior maps with information of where TLs are located in relation to the ego-vehicles location, as e.g. seen in [5].…”