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Abstract: Problem statement. Machine learning methods and neural networks are a promising tool for forecasting and identifying objects in real time, which allows for the application of such technologies in ensuring road traffic safety. The aim of the work is to develop a solution capable of detecting and classifying objects using artificial intelligence methods, implementing the functions of an intelligent driver assistance system onboard a vehicle. Methods used: creating an intelligent assistance service based on convolutional neural networks. An element of novelty in the presented solution is the implementation of a decision support service for the driver based on a compact low-power computing platform. Result. The selected service of the intelligent decision support system for drivers is implemented on a compact low-power computing device with an accuracy of 87 % based on the mean average precision (mAP) at an average frame rate of 32 frames per second. Practical significance. The presented solution allows for the implementation of a system using artificial intelligence algorithms on a vehicle base due to low energy consumption and a neuroprocessor module capable of working with video streams in real-time.
Keywords: Advanced Driver’s Assistance System, convolutional neural networks, artificial intelligence, machine learning, intelligent transportation systems, object detection.
Reference for citation
Vinitsky M., Dustalev E., Minin D., Babich V., Bobrovsky V. Development of an Advanced Driver Assistance System // Telecom IT. 2025. Vol. 13. Iss. 1. PP. 40‒46 (in Russian). DOI: 10.31854/2307-1303-2025-13-1-40-46. EDN: ZZGRGL
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M. Vinitsky,