Comparing fuzzy and intelligent PI controllers in stop-and-go manoeuvres

The aim of this work was twofold: on the one hand, to describe a comparative study of two intelligent control techniques-fuzzy and intelligent proportional-integral (PI) control, and on the other, to try to provide an answer to an as yet unsolved topic in the automotive sector-stop-and-go control in urban environments at very low speeds. Commercial vehicles exhibit nonlinear behavior and therefore constitute an excellent platform on which to check the controllers. This paper describes the design, tuning, and evaluation of the controllers performing actions on the longitudinal control of a car-the throttle and brake pedals-to accomplish stop-and-go manoeuvres. They are tested in two steps. First, a simulation model is used to design and tune the controllers, and second, these controllers are implemented in the commercial vehicle-which has automatic driving capabilities-to check their behavior. A stop-and-go manoeuvre is implemented with the two control techniques using two cooperating vehicles

On-line learning of a fuzzy controller for a precise vehicle cruise control system

Usually, vehicle applications require the use of artificial intelligent techniques to implement control methods, due to noise provided by sensors or the impossibility of full knowledge about dynamics of the vehicle (engine state, wheel pressure or occupiers weight). This work presents a method to on-line evolve a fuzzy controller for commanding vehicles? pedals at low speeds; in this scenario, the slightest alteration in the vehicle or road conditions can vary controller?s behavior in a non predictable way. The proposal adapts singletons positions in real time, and trapezoids used to codify the input variables are modified according with historical data. Experimentation in both simulated and real vehicles are provided to show how fast and precise the method is, even compared with a human driver or using different vehicles

Low-speed longitudinal controllers for mass-produced cars: A comparative study

Four longitudinal control techniques are compared: a classical Proportional-Integral (PI) control; an advanced technique-called the i-PI-that adds an intelligent component to the PI; a fuzzy controller based on human experience; and an adaptive-network-based fuzzy inference system. The controllers were designed to tackle one of the challenging topics as yet unsolved by the automotive sector: managing autonomously a gasoline-propelled vehicle at very low speeds. The dynamics involved are highly nonlinear and constitute an excellent test-bed for newly designed controllers. A Citroën C3 Pluriel car was modified to permit autonomous action on the accelerator and the brake pedals-i.e., longitudinal control. The controllers were tested in two stages. First, the vehicle was modeled to check the controllers' feasibility. Second, the controllers were then implemented in the Citroën, and their behavior under the same conditions on an identical real circuit was compared

Vision-based active safety system for automatic stopping

ntelligent systems designed to reduce highway fatalities have been widely applied in the automotive sector in the last decade. Of all users of transport systems, pedestrians are the most vulnerable in crashes as they are unprotected. This paper deals with an autonomous intelligent emergency system designed to avoid collisions with pedestrians. The system consists of a fuzzy controller based on the time-to-collision estimate – obtained via a vision-based system – and the wheel-locking probability – obtained via the vehicle’s CAN bus – that generates a safe braking action. The system has been tested in a real car – a convertible Citroën C3 Pluriel – equipped with an automated electro-hydraulic braking system capable of working in parallel with the vehicle’s original braking circuit. The system is used as a last resort in the case that an unexpected pedestrian is in the lane and all the warnings have failed to produce a response from the driver

Intelligent automatic overtaking system using vision for vehicle detection

There is clear evidence that investment in intelligent transportation system technologies brings major social and economic benefits. Technological advances in the area of automatic systems in particular are becoming vital for the reduction of road deaths. We here describe our approach to automation of one the riskiest autonomous manœuvres involving vehicles – overtaking. The approach is based on a stereo vision system responsible for detecting any preceding vehicle and triggering the autonomous overtaking manœuvre. To this end, a fuzzy-logic based controller was developed to emulate how humans overtake. Its input is information from the vision system and from a positioning-based system consisting of a differential global positioning system (DGPS) and an inertial measurement unit (IMU). Its output is the generation of action on the vehicle’s actuators, i.e., the steering wheel and throttle and brake pedals. The system has been incorporated into a commercial Citroën car and tested on the private driving circuit at the facilities of our research center, CAR, with different preceding vehicles – a motorbike, car, and truck – with encouraging results

Driving assistance system applied in curves for real vehicles | Sistema de ayuda a la conducción en curvas para vehículos reales

Decreasing the accidents on highway and urban environments is the main motivation for the research and developing of driving assistance systems, also called ADAS (Advanced Driver Assistance Systems). In recent years, there are many applications of these systems in commercial vehicles: ABS systems, Cruise Control (CC), parking assistance and warning systems (including GPS), among others. However, the implementation of driving assistance systems on the steering wheel is more limited, because of their complexity and sensitivity. This paper is focused in the development, test and implementation of a driver assistance system for controlling the steering wheel in curve zones. This system is divided in two levels: an inner control loop which permits to execute the position and speed target, softening the action over the steering wheel, and a second control outer loop (controlling for fuzzy logic) that sends the reference to the inner loop according the environment and vehicle conditions. The tests have been done in different curves and speeds. The system has been proved in a commercial vehicle with satisfactory results