Abstract
Advanced Driver Assistance Systems (ADAS) must undergo an extensive testing before they are put into production. But, testing on real vehicles is long, expensive, difficult to replicate and risky. In the future, it will always be necessary to use real vehicles for testing. But, this is not enough to meet all the requirements of reliability and safety. The self-driving will continue to make driving easier and safer. Nevertheless, the final question remains: what is the best evaluation method that will be able to verify the expected behavior and performance of the on-board systems in smart and autonomous cars? To do this, this article proposes several solutions, distributed in three parts. The first part “object detection architecture” depicts an approach for object detection based on YOLO with a good accuracy. The second “Lane detection architecture” is dedicated to detailed detection approach guidelines based on OpenCV. The last and third part “Traffic sign architecture” is dedicated to a detailed ConvNet approach to detection of signs based on CNN formed at OpenCV using the reverse propagation method. We achieved remarkable results, a real-time detection accuracy of 99.98%.
International Conference on Knowledge Science, Engineering and Management (KSEM 2022).
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Chniti, H., Mahfoudh, M. (2022). Designing a Model of Driving Scenarios for Autonomous Vehicles. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13369. Springer, Cham. https://doi.org/10.1007/978-3-031-10986-7_32
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