Real-Time AI-Enhanced Driver Assistance Systems

Abstract

The need to improve road traffic safety and to enhance the driving experience has played a significant role in the development of modern real-time AI-enhanced driver assistance systems. The goal of this essay is locally bound, intending to present and discuss a research plan aimed at integrating the following key components: artificial intelligence, learning algorithms, the driver’s physiology, monitoring, service-oriented systems, vehicle active safety and security, and wireless vehicle communications.

AI technologies have been traditionally used to manage complex autonomous vehicle functions, specifically dealing with the driving cycle. Less attention has been devoted to the AI role in providing reactive, proactive, and predictive functions to driver assistive systems. This represents an important step forward since AI is being used to predict severe accidents and to prevent them by offering timely and coordinated control actions. Also, there is an increasing growth of the integration of advanced and complex ICT tools in vehicle on-board systems and different automotive subsystems with distributed control. AI may be used to efficiently control and manage the demands of various automotive subsystems, resulting in a number of additional benefits, for example, design, complexity, reliability, safety, and flexibility. With the continuous need for innovation and the need for improved safety and system reactivity, it is timely to consider advanced methodologies and system architectures for the development of efficient real-time AI-enhanced driver assistance systems. An algorithmic research plan is proposed to address potential scientific and engineering solutions in this area.