Computer Vision Based Novel Steering Angle Calculation for Autonomous Vehicles

2018 ◽  
Author(s):  
Ranjith Rochan M. ◽  
Aarthi Alagammai K. ◽  
Sujatha J.
Keyword(s):  
Computer Vision ◽  
Autonomous Vehicles ◽  
Steering Angle ◽  
Angle Calculation

Enhanced navigation using computer vision-based steering angle calculation for autonomous vehicles

2018 ◽  
Vol 02 (01) ◽  
pp. 1850007
Author(s):  
M. Ranjith Rochan ◽  
K. Aarthi Alagammai ◽  
J. Sujatha
Keyword(s):  
Computer Vision ◽  
Autonomous Vehicles ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Steering Angle ◽  
Informative Feedback ◽  
Lighting Conditions ◽  
Region Extraction ◽  
Novel Method ◽  
Filtering Technique

A key requirement in the development of intelligent and driverless vehicles is steering angle computation for efficient navigation. This paper presents a novel method for computing steering angle for driverless vehicles using computer vision-based techniques of relatively lower computing cost. The proposed system consists of four major stages. The first stage includes dynamic road region extraction using Gaussian mixture model and expectation maximization algorithm. The second stage is to compute the steering angle based on the extracted road region. Subsequently, Kalman filtering technique is used to cancel spurious angle transition noises. In addition, future steering angle is estimated which in turn gives informative feedback for smooth navigation of the vehicle. The proposed algorithm was tested both on a simulator and real-time images and was found to give a good estimation of actual steering angle required for navigation. Further, it was also observed that this works in different lighting conditions as well as for both structured and unstructured road scenarios.

End-to-end imaging system optimization for computer vision in driving automation

2021 ◽  
Keyword(s):  
Computer Vision ◽  
International Symposium ◽  
Autonomous Vehicles ◽  
Fast Track ◽  
Imaging System ◽  
System Optimization ◽  
Electronic Imaging ◽  
End To End

Fast track article for IS&T International Symposium on Electronic Imaging 2021: Autonomous Vehicles and Machines 2021 proceedings.

scholarly journals Computer vision based obstacle detection and target tracking for autonomous vehicles

2021 ◽  
Vol 336 ◽  
pp. 07004
Author(s):  
Ruoyu Fang ◽  
Cheng Cai
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Target Tracking ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Pid Algorithm ◽  
Deep Learning Neural Network

Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.

From Neuron Coverage to Steering Angle: Testing Autonomous Vehicles Effectively

Computer ◽  
2021 ◽  
Vol 54 (8) ◽  
pp. 77-85
Author(s):  
Jack R Toohey ◽  
M S Raunak ◽  
David Binkley
Keyword(s):  
Autonomous Vehicles ◽  
Steering Angle

scholarly journals A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 163797-163817
Author(s):  
Usman Manzo Gidado ◽  
Haruna Chiroma ◽  
Nahla Aljojo ◽  
Saidu Abubakar ◽  
Segun I. Popoola ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Steering Angle

scholarly journals How Many Features is an Image Worth? Multi-Channel CNN for Steering Angle Prediction in Autonomous Vehicles

2021 ◽  
Author(s):  
Jason Munger ◽  
Carlos W. Morato
Keyword(s):  
Neural Networks ◽  
Autonomous Vehicles ◽  
Deep Neural Networks ◽  
Spatial Information ◽  
Image Data ◽  
Input Image ◽  
Steering Angle ◽  
Rgb Images ◽  
And Performance ◽  
The Individual

This project explores how raw image data obtained from AV cameras can provide a model with more spatial information than can be learned from simple RGB images alone. This paper leverages the advances of deep neural networks to demonstrate steering angle predictions of autonomous vehicles through an end-to-end multi-channel CNN model using only the image data provided from an onboard camera. Image data is processed through existing neural networks to provide pixel segmentation and depth estimates and input to a new neural network along with the raw input image to provide enhanced feature signals from the environment. Various input combinations of Multi-Channel CNNs are evaluated, and their effectiveness is compared to single CNN networks using the individual data inputs. The model with the most accurate steering predictions is identified and performance compared to previous neural networks.

scholarly journals DETECTION OF LANE LINES FOR SELFDRIVING CARS BY USING EFFECTIVE COMPUTER VISION TECHNIQUES WITH OPENCV IN PYTHON

2021 ◽  
pp. 69-72
Author(s):  
Aryan Verma
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Autonomous Vehicles ◽  
Semantic Segmentation ◽  
Visual Features ◽  
Traffic Lights ◽  
Classification Of Images ◽  
Pedestrian Crossing ◽  
Source Of Information

Presently computer vision is amongst the hottest topics in Artificial Intelligence and is being extensively used in Robotics, Detecting Objects, Classification of Images, Autonomous Vehicles & tracking, Semantic Segmentation along with photo correction in various apps. In Self driven cars/ vehicles, vision remains the main source of information for detecting lanes, traffic lights, pedestrian crossing and other visual features. [2]

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