The last decade witnessed a great development of automated driving vehicles (ADVs) and
vehicle intelligence. The significant increment of machine intelligence poses a new …

Driver distraction is one of the main causes of fatal traffic accidents. Therefore, the ability to
detect driver inattention is essential in building a safe yet intelligent transportation system …

This paper presents a local modification of the Levenberg-Marquardt algorithm (LM). First,
the mathematical basics of the classic LM method are shown. The classic LM algorithm is …

The appearance of fatigue is not conducive to driving activities because this state can affect
driving performance and even cause life-threatening consequences. To reduce various …

Autonomous vehicles want reliable and strong sensor suites and alert systems. This paper
discusses the composition and performance of a sophisticated monitoring and alert system …

The end of Moore's Law aligned with data privacy concerns is forcing machine learning (ML)
to shift from the cloud to the deep edge. In the next-generation ML systems, the inference …

With the current technological transformation in the automotive industry, autonomous
vehicles are getting closer to the Society of Automative Engineers (SAE) automation level 5 …

Driver drowsiness is a major contributor to road traffic accidents. A system capable of
detecting drowsiness and consequently warning drivers at an early stage could significantly …

In this paper1 a new neural networks training algorithm is presented. The algorithm
originates from the Recursive Least Squares (RLS) method commonly used in adaptive …

It is critical today to provide safe and collision-free transport. As a result, identifying the
driver's drowsiness before their capacity to drive is jeopardized. An automated hybrid …