In this paper the various driving style analysis solutions are investigated. An in-depth
investigation is performed to identify the relevant machine learning and artificial intelligence …

Driving is a social activity which involves endless interactions with other agents on the road.
Failing to locate these agents and predict their possible future actions may result in serious …

Since the advantage of hidden Markov model in dealing with time series data and for the
sake of identifying driving style, three driving style (aggressive, moderate and mild) are …

We present a flexible and scalable architecture that can learn to predict the future behavior
of a vehicle in inner-city traffic. While behavior prediction studies have mainly been focusing …

The rapid progress in IoT-and CPS-enabled devices and their convergence in the
Connected and Automated Mobility (CAM) sector have changed information and knowledge …

This article comprises a practical and original application of full neural predictors with fixed
prediction horizon in backward movements of a Truck-Trailer-Trailer prototype of a multi …

This article presents a new method and tools for the development of full neural predictors
and controllers, with fixed time horizon, based on static multilayer feedforward networks …

The aim of this work is to create a method to compute the steer direction of an autonomous
robot, moving in a road-like environment. It uses artificial neural networks to learn …

Driver behaviour has a significant influence on vehicle accidents. Measuring and providing
feedback on driver behaviour can provide significant benefits for understanding and …

The work considers road lane parameters that correlate with steering angle of a car and
which are suitable for accurate prediction of steering signal using neural network technique …