Autonomous Car Using Deep Learning and Image Processing Technology
Keywords:
Self-driving, Autopilot, Haar Cascade, Deep learning, Image processing, Autonomous, Grayscale image, Canny edge detection, Open CV, Manual controlAbstract
This study showcases a mini version of autonomous cars where it includes both autopilot mode and manual control. Autopilot mode is implemented using deep learning and image processing techniques, such as CNN, Haar cascade model and mobile app for manual control. IoT plays an important role. The Raspberry pi, pi camera and Arduino uno work as the main processors. The high-resolution pi camera, which is of 8 MP, will provide real time information in the form of photos as input to raspberry pi which is mainly responsible for controlling the car based on its input, which in turn results in obstacle detection, traffic sign detection, stop sign detection and lane detection by using deep learning techniques. Along with these features, the car is able to overtake the other car if it comes across a lane with proper indicators.
References
Sanil N, Rakesh V, Mallapur R, Ahmed MR. Deep learning techniques for obstacle detection and avoidance in driverless cars. In 2020 IEEE International Conference on Artificial Intelligence and Signal Processing (AISP). 2020 Jan 10; 1–4.
Kore P, Khoje S. Obstacle detection for auto-driving using convolutional neural network. In Proceedings of the 2nd International Conference on Data Engineering and Communication Technology: ICDECT 2017. Singapore: Springer; 2019; 269–278.
Abed Yasir Fatha, Ashiqul Islam HM, Azim Anowarul, Hasan Md. Abeed, Dutta Tonushree. Project Report on the Prototype of an Automated Self Driving Vehicle. Bangladesh: Bangladesh University of Engineering & Technology; 2021 Jan. 10.13140/RG.2.2.28281.08809.
Sharma C, Bharathiraja S, Anusooya G. Self Driving Car using Deep Learning Technique. Int J Eng Res Technol (IJERT). 2020 Jun; 9(6): 248–253.
Gupta A, Anpalagan A, Guan L, Khwaja AS. Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues. Array. 2021 Jul 1; 10: 100057.
Kulkarni R, Dhavalikar S, Bangar S. Traffic light detection and recognition for self driving cars using deep learning. In 2018 IEEE 4th International Conference on Computing Communication Control and Automation (ICCUBEA). 2018 Aug 16; 1–4.
Aneesh AN, Shine L, Pradeep R, Sajith V. Real time traffic light detection and recognition based on deep retinanet for self driving cars. In 2019 IEEE 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). 2019 Jul 5; 1: 1554–1557.
Prajwal P, Harish DH, Gajanana R, Jayasri BS, Lokesh S. Object Detection in Self Driving Cars Using Deep Learning. 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India. 2021 Dec; 1–7.
Duy Anh Tran, Pascal Fisher, Alen Smajic, Yujin So. Real-time Object Detection for Autonomous Driving using Deep Learning. Frankfurt, Germany: Goethe-Universität Frankfurt am Main; 2021 Mar; 1–14.
Riadh Ayachi, Yahia Said, Mohamed Atri. To perform Road Signs Recognition for Autonomous Vehicles Using Cascaded Deep Learning. Artif Intell Adv. 2019 Jul; 1(1): 1–10.
George-Zamfir Tiron, Marian Silviu Poboroniuc. Neural Network Based Traffic Sign Recognition for Autonomous Driving. 2019 International Conference on Electromechanical and Energy Systems (SIELMEN), Craiova, Romania. 2019 Nov; 1–5.