Abstract-This paper describes the Generic Obstacle and Lane Detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety. Based on a full-custom massively parallel hardware, it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings) at a rate of 10 Hz. Thanks to a geometrical transform supported by a specific hardware module, the perspective effect is removed from both left and right stereo images; the left is used to detect lane markings with a series of morphological filters, while both remapped stereo images are used for the detection of free-space in front of the vehicle. The output of the processing is displayed on both an onboard monitor and a control-panel to give visual feedbacks to the driver. The system was tested on the mobile laboratory (MOB-LAB) experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km/h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement.

Recently, a large emphasis has been devoted to Automatic Vehicle Guidance since the automation of driving tasks carries a large number of benefits, such as the optimization of the use of transport infrastructures, the improvement of mobility, the minimization of risks, travel time, and energy consumption. This paper surveys the most common approaches to the challenging task of Autonomous Road Following reviewing the most promising experimental solutions and prototypes developed worldwide using AI techniques to perceive the environmental situation by means of artificial vision.The most interesting results and trends in this field as well as the perspectives on the evolution of intelligent vehicles in the next decades are also sketched out.

The last few decades witnessed the birth and growth of a new sensibility to transportation efficiency. In particular, the need for efficient and improved people and goods mobility pushed researchers to address the problem of intelligent transportation systems. This paper surveys the most advanced approaches to the (partial) customization of road following task, using on-board systems based on artificial vision. The functionalities of lane detection, obstacle detection and pedestrian detection are described and classified, and their possible application on future road vehicles is discussed.

This paper presents the method for detecting pedestrian recently implemented on the ARGO vehicle. The perception of the environment is performed through the sole processing of images acquired from a vision system installed on board of the vehicle: the analysis of a monocular image delivers a first coarse detection, while a distance refinement is performed thanks to a stereo vision technique.

This paper details filtering subsystem for a tetravision based pedestrian detection system. The complete system is based on the use of both visible and far infrared cameras; in an initial phase it produces a list of areas of attention in the images which can contain pedestrians. This list is furtherly refined using symmetry-based assumptions. Then, this results is fed to a number of independent validators that evaluate the presence of human shapes inside the areas of attention.Histogram of oriented gradients and Support Vector Machines are used as a filter and demonstrated to be able to successfully classify up to 91% of pedestrians in the areas of attention.

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This paper presents a new motion planning primitive to be used for the iterative steering of vision-based autonomous vehicles. This primitive is a parameterized quintic spline, denoted as-spline, that allows interpolating an arbitrary sequence of points with overall second-order geometric (2-) continuity. Issues such as completeness, minimality, regularity, symmetry, and flexibility of these 2-splines are addressed in the exposition. The development of the new primitive is tightly connected to the inversion control of nonholonomic car-like vehicles. The paper also exposes a supervisory strategy for iterative steering that integrates feedback vision data processing with the feedforward inversion control.

This paper presents the methods for sensing vehicles (localization and tracking) implemented on the ARGO vehicle. The perception of the environment is performed through the sole processing of images acquired from a stereo vision system installed on board of the vehicle.