Convolutional Neural Network based Working Model of Self Driving Car - a Study

Chaitra, P G; Deepthi, .; Gautami, S; Suraj, H M; Kumar, Naveen

doi:10.1109/icesc48915.2020.9155826

Cited by 12 publications

(1 citation statement)

References 3 publications

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“…Chaitra et al introduced the integration of deep learning technology (such as CNN and the Hough transform algorithm) with Open CV in self-driving vehicles and sensors to detect lanes, lane objects, and traffic signals. The results revealed that the automatic driver assistance system controls the stability and safety of ESDVs [16]. Chen introduced a cruise control and lane change control technology for self-driving vehicles, focusing on vehicle steering control, acceleration, and deceleration control based on PID, which rapidly converges control signals to ensure the stability of ESDVs [17].…”

Section: Introductionmentioning

confidence: 99%

Applying a Novel Image Recognition Curve-Fitting Control Strategy Combined with a Cloud Monitoring Technique into an Electric Self-Driving Vehicle (ESDV) to Improve Its Operation Efficiency

Liu,

Lin,

Lai

et al. 2023

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This study aims to develop an image recognition curve-fitting (IRCF) control strategy integrated with a cloud monitoring technique for application in electric self-driving vehicles (ESDVs) to improve their operation efficiency. The study focuses on an electric vehicle designed to reduce the carbon emissions and promote sustainability. The main camera, combined with the IRCF control strategy, was used to control the ESDV to enhance its operational efficiency. The proposed ESDV employs a pair of cameras to capture images and transmit them to the cloud-based web monitoring platform in real time. This allows the researchers to adjust the control parameters and promptly remove the road obstacles. The ESDV is equipped with a horn, two ultrasonic sensors, and an LED display, which can instantly detect the obstacles ahead of and behind the vehicle. When there are obstacles on the road, the vehicle will automatically stop, and the LED display will provide a visual representation of the obstacles, accompanied by the sounding of the horn as a warning signal. Meanwhile, the secondary camera detects the signal mark and feeds it back to the LED display, thereby informing passengers and other road users about the prevailing driving conditions. The proposed IRCF control strategy was compared with the traditional Hough line detection method on a 110 m ring road. The results revealed that the proposed control strategy outperformed the traditional Hough line detection method in terms of speed, efficiency, and running dexterity. Therefore, integrating the proposed control strategy into the automatic assistance driving system can improve the ESDV’s operation efficiency. Furthermore, the combination of the obstacle detection and signal sign detection functions for the ESDV used in this study can better fulfill the actual ESDV operation requirements on the road.

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Section: Introductionmentioning

confidence: 99%