Open AccessUnderstanding the Perception of Road Segmentation and Traffic Light Detection u sing Machine Learning Techniques
Author(s) -
Anusha A. Nandargi*,
Ashwini Bagewadi,
Amey Joshi,
Ajinkya Jadhav,
Arun Tigadi
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a3103.059120
Subject(s) - segmentation , advanced driver assistance systems , convolutional neural network , computer science , artificial intelligence , field programmable gate array , computer vision , perception , original equipment manufacturer , automotive industry , engineering , computer hardware , neuroscience , biology , aerospace engineering , operating system
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.