Lane Detection and Traffic Sign for Autonomous Cars
Autor: | D. Divya Kalpana, K. Keerthi, B. Vishnu Priya, Ch. Mutyalanna, B. Malleswari |
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Jazyk: | angličtina |
Rok vydání: | 2023 |
Předmět: | |
DOI: | 10.5281/zenodo.7541116 |
Popis: | Autonomous Vehicles are another term for self-driving cars. This vehicle can detect its surroundings. These perceived limits processed, and also varied actuators within the automobile operate with none human intervention. These perceived limits are managed, therefore numerous actuators within automotive operate with none human intervention. All Automated Functions are performed by autonomous vehicles using sensors, actuators, machine learning algorithms, and software. Autonomous vehicles are heavily reliant on software. The software architecture connects the hardware to the application. AUTOSAR is the acronym for automotive vehicle standardized software. The AUTOSAR architecture standardizes application software and hardware. All of the necessary software, including the run-time environment, device drivers, and basic programs, are included this standardized architecture. In Self-Driving Cars, there are two critical modules. Both operate automatically and without human intervention. In this context, a machine learning algorithm is offered. This algorithmic rule primarily won’t train form models and discover shapes for traffic sign-revealing and lanerevealing. Each chores written in python and use the Opencv2 Library file, the num.py library file therefore the Hough detection technique to detect applicable stoplight discs. All of those tools are used to train shape models employing a supervised coaching algorithm, and the revealing completed such a way that self-ruling cars will spot lane and traffic- signs. {"references":["Bimbraw, K. (2015, July). Autonomous cars: Past, present and future a review of the developments in the last century, the present scenario and the expected future of autonomous vehicle technology. In 2015 12th international conference on informatics in control, automation and robotics (ICINCO) (Vol. 1, pp. 191- 198). IEEE.","Maro, S., Anjorin, A., Wohlrab, R., & Steghöfer, J. P. (2016, August). Traceability maintenance: factors and guidelines. In Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering (pp. 414-425).","Zabavnik, J., & Riel, A. (2019). Knowledge and skills requirements for the software design and testing of automotive applications. arXiv preprint arXiv:1910.13128.","Deshpande, P. (2014). Road safety and accident prevention in India: a review. International journal of advanced engineering technology, 5(2), 64-68.","Fayjie, A. R., Hossain, S., Oualid, D., & Lee, D. J. (2018, June). Driverless car: Autonomous driving using deep reinforcement learning in urban environment. In 2018 15th international conference on ubiquitous robots (ur) (pp. 896-901). IEEE.","Agafonov, A., & Yumaganov, A. (2020, May). 3D Objects Detection in an Autonomous Car Driving Problem. In 2020 International Conference on Information Technology and Nanotechnology (ITNT) (pp. 1-5). IEEE.","Hussain, R., & Zeadally, S. (2018). Autonomous cars: Research results, issues, and future challenges. IEEE Communications Surveys & Tutorials, 21(2), 1275-1313.","Ikhlayel, M., Iswara, A. J., Kurniawan, A., Zaini, A., & Yuniarno, E. M. (2020, November). Traffic Sign Detection for Navigation of Autonomous Car Prototype using Convolutional Neural Network. In 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) (pp. 205-210). IEEE.","Mík, A. J., & Bouchner, B. P. (2020, June). Safety of crews of autonomous cars. In 2020 Smart City Symposium Prague (SCSP) (pp. 1-5). IEEE.","Sivakumar, P., Devi, R. S., Buvanesswaran, A. D., Kumar, B. V., Raguram, R., & Ranjithkumar, M. (2020, July). Model-Based Testing of Car Engine Start/Stop Button Debouncer Model. In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 1077-1082). IEEE."]} |
Databáze: | OpenAIRE |
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