Tailored AI Solutions For Embedded Control And Driver Assistance Systems In Automotive Electronics
DOI:
https://doi.org/10.63278/jicrcr.vi.3633Abstract
The application of artificial intelligence to automotive embedded control systems, and driver assistance technologies is a radical change in the design and functioning of a vehicle. The current-day cars have become a complex computing platform on which smart programs run to stream sensor information through smart algorithms to allow autopilot decision-making skills and adaptive assistive systems. The embedded systems are empowered with deep learning methodologies, predictive control mechanisms, sensor fusion techniques to interpret the complex driving environments in real time to enable facilitation of the highway automation, adaptive cruise control, urban traffic management, and personalized driver support. These technological innovations provide tremendous value such as reduction in accidents, increased fuel consumption, better user interface, and financial development in the automobile industry. Nonetheless, there are a number of critical issues, including hardware computational limits, systems reliability mandates, regulatory consequences, intricacies, human factors issues, and data collection problems. Processors of automotive standards are required to work under extreme constraints of processing power, memory capacity, and thermal management and meet high standards of safety. The use of probabilistic AI causes verification challenges to safety-critical applications, and different groups of users have different levels of technology acceptance. Creation of large scale annotated data is resource-intensive, and edge case representation is also inadequate in training data. The shift to software-defined cars brings new cybersecurity issues and has a chance of ongoing progress with an option of over-the-air development. It is these complex issues that demand novel hardware architectures, sound validation schemes, clear human-machine interfaces, and holistic strategies on data that will guarantee AI-enabled vehicles provide safer and efficient transportation solutions with wider applicability and user demographics.




