This work describes a color Vision-based System intended to perform stable autonomous driving on unmarked roads. Accordingly, this implies the development of an accurate road surface detection system that ensures vehicle stability. Although this topic has already been documented in the technical literature by different research groups, the vast majority of the already existing Intelligent Transportation Systems are devoted to assisted driving of vehicles on marked extra urban roads and highways. The complete system was tested on the BABIECA prototype vehicle, which was autonomously driven for hundred of kilometers accomplishing different navigation missions on a private circuit that emulates an urban quarter. During the tests, the navigation system demonstrated its robustness with regard to shadows, road texture, and weather and changing illumination conditions.

/pdf/a-color-vision-based-lane-tracking-system-for-autonomous-2jig37bklh.pdf

A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked Roads

It is known that the techniques under the topic of Soft Computing have a strong capability of learning and cognition, as well as a good tolerance to uncertainty and imprecision. Due to these properties they can be applied successfully to Intelligent Vehicle Systems; ITS is a broad range of technologies and techniques that hold answers to many transportation problems. The unmanned control of the steering wheel of a vehicle is one of the most important challenges facing researchers in this area. This paper presents a method to adjust automatically a fuzzy controller to manage the steering wheel of a mass-produced vehicle; to reach it, information about the car state while a human driver is handling the car is taken and used to adjust, via iterative genetic algorithms an appropriated fuzzy controller. To evaluate the obtained controllers, it will be considered the performance obtained in the track following task, as well as the smoothness of the driving carried out.

/pdf/automatic-lateral-control-for-unmanned-vehicles-via-genetic-58aboe9tjb.pdf

Automatic lateral control for unmanned vehicles via genetic algorithms

Much work on fuzzy control, covering research, development and applications, has been developed in Europe since the 90's. Nevertheless, the existing books in the field are compilations of articles without interconnection or logical structure or they express the personal point of view of the author. This book compiles the developments of researchers with demonstrated experience in the field of fuzzy control following a logic structure and a unified the style. The first chapters of the book are dedicated to the introduction of the main fuzzy logic techniques, where the following chapters focus on concrete applications. This book is supported by the EUSFLAT and CEA-IFAC societies, which include a large number of researchers in the field of fuzzy logic and control. The central topic of the book, Fuzzy Control, is one of the main research and development lines covered by these associations.

/pdf/fuzzy-modeling-and-control-theory-and-applications-575kcx2y4l.pdf

Fuzzy Modeling and Control: Theory and Applications

The vision-based vehicle detection in front of an ego-vehicle is regarded as promising for driver assistance as well as for autonomous vehicle guidance. The feasibility of vehicle detection in a passenger car requires accurate and robust sensing performance. A multivehicle detection system based on stereo vision has been developed for better accuracy and robustness. This system utilizes morphological filter, feature detector, template matching, and epipolar constraint techniques in order to detect the corresponding pairs of vehicles. After the initial detection, the system executes the tracking algorithm for the vehicles. The proposed system can detect front vehicles such as the leading vehicle and side-lane vehicles. The position parameters of the vehicles located in front are obtained based on the detection information. The proposed vehicle detection system is implemented on a passenger car, and its performance is verified experimentally.

Vision-based vehicle detection and tracking algorithm design

This paper presents techniques, related experiments and real results (not simulations) performed with two unmanned vehicles. The test zone is a private circuit with the characteristics of an urban track. The vehicles are common mass-produced cars, provided with automatic actuators operating on the car controls (power-assisted steering and accelerator). These actuators work commanded by a fuzzy logic based control system. The input information to the control system is provided by three elements: high precision GNSS, car tachometer and a wireless network environment.

Frontal and lateral control for unmanned vehicles in urban tracks

This document presents a fuzzy control application in the unmanned driving field. Two electric cars have been conveniently instrumented in order to transform them into platforms for automatic driving experiments. Onboard speed and steering fuzzy controllers are the core of the guiding system. Navigation is essentially DGPS-based providing obstacles detection and avoidance by means of artificial vision in a reactive manner.

Vehicle fuzzy driving based on DGPS and vision

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Vehicle fuzzy driving based on DGPS and vision