Abstract
This study presents a software model that identifies vehicles in front of a test vehicle, measures distances, and classifies them as safe, slow speed, and brake. The classification determines which signal should be transmitted to the control unit. A specially tailored dataset of 2162 images from Bangladesh’s roadside is used for the software model, which uses transfer learning to identify frontal objects, estimate distances, and classify distances according to control unit signals. Furthermore, two microcontrollers are used for hardware systems, utilizing an ultrasonic sensor to calculate distances, identify frontal objects, and show the expected outputs. The AURIX TC375 microcontroller board-control unit receives signals and triggers the appropriate output. This system can serve as a foundation for autonomous vehicle safety research.
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Islam, F., Nileema, T.T., Abir, F.R., Jidney, T.T., Kalpoma, K.A. (2024). An Automotive ECU-Based Forward Collision Prevention System. In: Das, S., Saha, S., Coello Coello, C.A., Bansal, J.C. (eds) Advances in Data-Driven Computing and Intelligent Systems. ADCIS 2023. Lecture Notes in Networks and Systems, vol 892. Springer, Singapore. https://doi.org/10.1007/978-981-99-9521-9_33
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DOI: https://doi.org/10.1007/978-981-99-9521-9_33
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