Neural network-based fuzzy inference system for speed control of heavy duty vehicles with electronic throttle control system

Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control

This article deals with the use of neural networks for estimation of deceleration model parameters for the adaptive cruise control unit. The article describes the basic functionality of adaptive cruise control and creates a mathematical model of braking, which is one of the basic functions of adaptive cruise control. Furthermore, an analysis of the influences acting in the braking process is performed, the most significant of which are used in the design of deceleration prediction for the adaptive cruise control unit using neural networks. Such a connection using artificial neural networks using modern sensors can be another step towards full vehicle autonomy. The advantage of this approach is the original use of neural networks, which refines the determination of the deceleration value of the vehicle in front of a static or dynamic obstacle, while including a number of influences that affect the braking process and thus increase driving safety.

Design and Comparative Study of Advanced Adaptive Control Schemes for Position Control of Electronic Throttle Valve

This paper investigates the performance of two different adaptive control schemes for controlling the angular position of an electronic throttle (ET) plate. The adaptive backstepping controller and adaptive sliding mode backstepping controller are the controllers under consideration. The control design based on these adaptive controllers is firstly addressed and the stability analysis of each controller has been presented and the convergence of both position and estimation errors for both controllers have been proved. A comparison study of the performance of both controllers has been conducted in terms of system transient characteristics and the behavior of their associated adaptive gain. The simulation has been implemented within the environment of the MATLAB package.

Safety impact of right-turn waiting area at signalised junctions conditioned on driver's decision-making based on fuzzy cellular automata

Right-turn waiting area (RWA) is a short demarcated queueing area ahead of the stop line that allows the right-turn vehicles at signalised junctions under the permissive filtering signal operation to proceed into the junction-box at the onset of full green signal phase. The RWA layout gives guidance to vehicle placement of turning vehicles which improves safety and mitigates vehicle queue overflow of the right-turn vehicles. RWA enhances the capacity of right-turn lanes while alleviating conflict severity in some cases. This study analysed the safety impact of the conflict between opposing straight-through vehicles and right-turn vehicles at RWA junctions in Singapore. A microscopic simulation model based on Fuzzy Cellular Automata (FCA) was developed. Field surveys were carried out to obtain the inputs for calibrating the fuzzy inference system. Different scenarios were analysed and discussed including different types of junctions with and without RWAs. The proposed model is found to be able to simulate decision-making of individual drivers at RWA or before stop line, such as exiting the queueing area or crossing the stop line when faced with different gaps and velocity of opposing straight-through vehicles.