GPS satellite (4) ranging signals (6) received (32) on comm1, and DGPS auxiliary range correction signals and pseudolite carrier phase ambiguity resolution signals (8) from a fixed known earth base station (10) received (34) on comm2, at one of a plurality of vehicles/aircraft/automobiles (2) are computer processed (36) to continuously determine the one's kinematic tracking position on a pathway (14) with centimeter accuracy. That GPS-based position is communicated with selected other status information to each other one of the plurality of vehicles (2), to the one station (10), and/or to one of a plurality of control centers (16), and the one vehicle receives therefrom each of the others' status information and kinematic tracking position. Objects (22) are detected from all directions (300) by multiple supplemental mechanisms, e.g., video (54), radar/lidar (56), laser and optical scanners. Data and information are computer processed and analyzed (50,52,200,452) in neural networks (132, FIGS. 6-8) in the one vehicle to identify, rank, and evaluate collision hazards/objects, an expert operating response to which is determined in a fuzzy logic associative memory (484) which generates control signals which actuate a plurality of control systems of the one vehicle in a coordinated manner to maneuver it laterally and longitudinally to avoid each collision hazard, or, for motor vehicles, when a collision is unavoidable, to minimize injury or damage therefrom. The operator is warned by a heads up display and other modes and may override. An automotive auto-pilot mode is provided.

GPS vehicle collision avoidance warning and control system and method

Reliable understanding of the 3D driving environment is vital for obstacle detection and adaptive cruise control (ACC) applications. Laser or millimeter wave radars have shown good performance in measuring relative speed and distance in a highway driving environment. However the accuracy of these systems decreases in an urban traffic environment as more confusion occurs due to factors such as parked vehicles, guardrails, poles and motorcycles. A stereovision based sensing system provides an effective supplement to radar-based road scene analysis with its much wider field of view and more accurate lateral information. This paper presents an efficient solution using a stereovision based road scene analysis algorithm, which employs the "U-V-disparity" concept. This concept is used to classify a 3D road scene into relative surface planes and characterize the features of road pavement surfaces, roadside structures and obstacles. Real-time implementation of the disparity map calculation and the "U-V-disparity" classification is also presented.

U-V-disparity: an efficient algorithm for stereovision based scene analysis

Reliable understanding of the 3D driving environment is vital for obstacle detection and adaptive cruise control (ACC) applications. Laser or millimeter wave radars have shown good performance in measuring relative speed and distance in a highway driving environment. However the accuracy of these systems decreases in an urban traffic environment as more confusion occurs due to factors such as parked vehicles, guardrails, poles and motorcycles. A stereovision based sensing system provides an effective supplement to radar-based road scene analysis with its much wider field of view and more accurate lateral information. This paper presents an efficient solution using a stereovision based road scene analysis algorithm which employs the "U-V-disparity" concept. This concept is used to classify a 3D road scene into relative surface planes and characterize the features of road pavement surfaces, roadside structures and obstacles. Real-time implementation of the disparity map calculation and the "U-V-disparity" classification is also presented.

A complete U-V-disparity study for stereovision based 3D driving environment analysis

An object detection device, including: an imaging unit (400) that is mounted on a movable body; an object detection unit (201) that calculates an image displacement of a partial image between two images captured by the imaging unit (400) at different times, and performs detection processing to detect an object in an image based on at least the image displacement; and a control unit (201) that changes a manner of performing the detection processing based on a position in the image in a lateral direction of the movable body.

Object detection device

A practical real-time system for passive obstacle detection and avoidance is presented. Range information is obtained from stereo images by first computing a disparity picture from the image pair and extracting points above the ground plane. Then these points are projected onto the ground plane and an Instantaneous Obstacle Map (IOM) is obtained. The IOM is transformed into a one dimensional steering vector that represents the hindrance associated with steering in a particular direction and then a one dimensional search is performed on the steering vector for an angle with least hindrance. The steering direction and hindrance value are used to set the speed of the vehicle. This system has been implemented on the Mobile Perception Lab (MPL) at University of Massachusetts at Amherst with considerable success, running at 2 Hz for 256/spl times/240 sized images. >

A practical obstacle detection and avoidance system

A novel approach to obstacle detection using optical flow without recovering range information has been developed. This method can be used for ground vehicles to navigate through man-made roadways or natural outdoor terrain or for air vehicles to land on known or unknown terrain. A linear relationship, plotted as a line and called a reference flow line, has been found. This reference flow line can be used to detect discrete obstacles above or below the reference terrain. Slopes of surface regions are also computed. The approach is simple, fast, and robust because: (1) the only required information is one component of the optical flow, (2) each image line can be processed in parallel, and (3) the error sources involved are reduced to a minimum. An initial experiment using noisy synthetic data is also included to demonstrate the applicability and robustness of the method. >

/pdf/obstacle-detection-for-a-vehicle-using-optical-flow-3owjlvl0al.pdf

G.-S. Young

Papers

Obstacle detection for a vehicle using optical flow