A Robust QR and Computer Vision-Based Sensorless Steering Angle Control, Localization, and Motion Planning of Self-Driving Vehicles

Autonomous path following has gained tremendous popularity during the last few decades. Numerous researchers have contributed to the development of highly automated navigation systems using different types of sensors and their combination. However, their proposed approaches do not provide a cost-eff...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.151766-151774
Main Authors: Abid, Samia, Hayat, Bashir, Shafique, Sarmad, Ali, Zain, Ahmed, Bilal, Riaz, Faisal, Sung, Tae-Eung, Kim, Ki-Il
Format: Article
Language:English
Subjects:
Autonomous cars
Cameras
Computer vision
Customization
lane detection
Lane keeping
Laser radar
Localization
Location awareness
Motion planning
Navigation systems
Object recognition
Planning
quick response
Robots
Sensorless steering angle control
Sensors
Steering
Three-dimensional displays
Trajectory planning
Vehicles
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Summary:Autonomous path following has gained tremendous popularity during the last few decades. Numerous researchers have contributed to the development of highly automated navigation systems using different types of sensors and their combination. However, their proposed approaches do not provide a cost-efficient solution because of the deployment of exorbitant and sophisticated sensors, which remains a challenging problem for customized vehicles used in academic research. To overcome this issue, this study presents an economically efficient sensorless steering angle approach that employs a single camera for steering control and quick response (QR) based localization of a vehicle. Moreover, we used SONAR for object detection in a defined route to avoid possible collisions. The proposed technique combines a Probablistic Hough Transfrom for lane detection and QR codes, which helps the vehicle stay in its lane for stabilized control. To prove the efficiency of our approach, we tested it on our developed prototype vehicle named EMO. To validate the proposed approach through in-field testing, we designed a customized test track within the campus. The experimental results show the benefit of our proposed approach compared to existing methods available in the literature.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3124636