Automatic Traffic Light Detection for Self-Driving Cars Using Transfer Learning

2022 ◽  
pp. 597-606
Author(s):  
S. Gautam ◽  
A. Kumar
Keyword(s):  
Transfer Learning ◽  
Traffic Light ◽  
Light Detection ◽  
Self Driving Cars

scholarly journals Understanding the Perception of Road Segmentation and Traffic Light Detection using Machine Learning Techniques

2020 ◽  
Vol 9 (1) ◽  
pp. 2698-2704
Keyword(s):  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Machine Learning Techniques ◽  
Traffic Light ◽  
Driver Assistance Systems ◽  
Light Detection ◽  
Detection Model ◽  
Driving Assistance ◽  
Self Driving Cars ◽  
Road Segmentation

Advanced Driving Assistance System (ADAS) has seen tremendous growth over the past 10 years. In recent times, luxury cars, as well as some newly emerging cars, come with ADAS application. From 2014, Because of the entry of the European new car assessment programme (EuroNCAP) [1] in the AEBS test, it helped gain momentum the introduction of ADAS in Europe [1]. Most OEMs and research institutes have already demonstrated on the self-driving cars [1]. So here, a focus is made on road segmentation where LiDAR sensor takes in the image of the surrounding and where the vehicle should know its path, it is fulfilled by processing a convolutional neural network called semantic segmentation on an FPGA board in 16.9ms [3]. Further, a traffic light detection model is also developed by using NVidia Jetson and 2 FPGA boards, collectively named as 'Driving brain' which acts as a super computer for such networks. The results are obtained at higher accuracy by processing the obtained traffic light images into the CNN classifier [5]. Overall, this paper gives a brief idea of the technical trend of autonomous driving which throws light on algorithms and for advanced driver-assistance systems used for road segmentation and traffic light detection

HSI COLOR CONVERSION AND HOG FEATURE EXTRACTION IN TRAFFIC LIGHT DETECTION USING AdaBSF

2017 ◽  
Vol 5 (3) ◽  
pp. 20
Author(s):  
JEBISHA J ◽  
MONISHA V ◽  
JEMI B. FEMILA ◽  
◽  
◽  
...  
Keyword(s):  
Feature Extraction ◽  
Traffic Light ◽  
Light Detection ◽  
Color Conversion

scholarly journals Prototyping a Traffic Light Recognition Device with Expert Knowledge

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 278 ◽  
Author(s):  
Thiago Almeida ◽  
Hendrik Macedo ◽  
Leonardo Matos ◽  
Nathanael Vasconcelos
Keyword(s):  
Expert Knowledge ◽  
Support Vector ◽  
Background Suppression ◽  
Processing Unit ◽  
Driver Assistance ◽  
Traffic Light ◽  
Accuracy Rate ◽  
Light Detection ◽  
Vector Machines ◽  
Detection And Recognition

Traffic light detection and recognition (TLR) research has grown every year. In addition, Machine Learning (ML) has been largely used not only in traffic light research but in every field where it is useful and possible to generalize data and automatize human behavior. ML algorithms require a large amount of data to work properly and, thus, a lot of computational power is required to analyze the data. We argue that expert knowledge should be used to decrease the burden of collecting a huge amount of data for ML tasks. In this paper, we show how such kind of knowledge was used to reduce the amount of data and improve the accuracy rate for traffic light detection and recognition. Results show an improvement in the accuracy rate around 15%. The paper also proposes a TLR device prototype using both camera and processing unit of a smartphone which can be used as a driver assistance. To validate such layout prototype, a dataset was built and used to test an ML model based on adaptive background suppression filter (AdaBSF) and Support Vector Machines (SVMs). Results show 100% precision rate and recall of 65%.

scholarly journals TRAFFIC LIGHT DETECTION USING CONIC SECTION GEOMETRY

2016 ◽  
Vol III-1 ◽  
pp. 191-200
Author(s):  
S. Hosseinyalmdary ◽  
A. Yilmaz
Keyword(s):  
Prior Knowledge ◽  
Pose Estimation ◽  
Open Problem ◽  
Autonomous Vehicles ◽  
Conic Section ◽  
Traffic Light ◽  
Light Detection ◽  
State Recognition ◽  
Traffic Lights ◽  
Stereo Cameras

Traffic lights detection and their state recognition is a crucial task that autonomous vehicles must reliably fulfill. Despite scientific endeavors, it still is an open problem due to the variations of traffic lights and their perception in image form. Unlike previous studies, this paper investigates the use of inaccurate and publicly available GIS databases such as OpenStreetMap. In addition, we are the first to exploit conic section geometry to improve the shape cue of the traffic lights in images. Conic section also enables us to estimate the pose of the traffic lights with respect to the camera. Our approach can detect multiple traffic lights in the scene, it also is able to detect the traffic lights in the absence of prior knowledge, and detect the traffics lights as far as 70 meters. The proposed approach has been evaluated for different scenarios and the results show that the use of stereo cameras significantly improves the accuracy of the traffic lights detection and pose estimation.

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