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

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 on-board 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.

/pdf/gold-a-parallel-real-time-stereo-vision-system-for-generic-3mpgpjb6f8.pdf

GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection

Driver-assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively considered. It is therefore important to take a critical look at key aspects of these systems, one of which is lane-position tracking. It is for these driver-assistance objectives that motivate the development of the novel "video-based lane estimation and tracking" (VioLET) system. The system is designed using steerable filters for robust and accurate lane-marking detection. Steerable filters provide an efficient method for detecting circular-reflector markings, solid-line markings, and segmented-line markings under varying lighting and road conditions. They help in providing robustness to complex shadowing, lighting changes from overpasses and tunnels, and road-surface variations. They are efficient for lane-marking extraction because by computing only three separable convolutions, we can extract a wide variety of lane markings. Curvature detection is made more robust by incorporating both visual cues (lane markings and lane texture) and vehicle-state information. The experiment design and evaluation of the VioLET system is shown using multiple quantitative metrics over a wide variety of test conditions on a large test path using a unique instrumented vehicle. A justification for the choice of metrics based on a previous study with human-factors applications as well as extensive ground-truth testing from different times of day, road conditions, weather, and driving scenarios is also presented. In order to design the VioLET system, an up-to-date and comprehensive analysis of the current state of the art in lane-detection research was first performed. In doing so, a comparison of a wide variety of methods, pointing out the similarities and differences between methods as well as when and where various methods are most useful, is presented

/pdf/video-based-lane-estimation-and-tracking-for-driver-57a6ptp34w.pdf

Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation

https://www.cbica.upenn.edu/sbia/papers/384.pdf

Lane detection and tracking using B-Snake

The problem of road or lane perception is a crucial enabler for advanced driver assistance systems. As such, it has been an active field of research for the past two decades with considerable progress made in the past few years. The problem was confronted under various scenarios, with different task definitions, leading to usage of diverse sensing modalities and approaches. In this paper we survey the approaches and the algorithmic techniques devised for the various modalities over the last 5 years. We present a generic break down of the problem into its functional building blocks and elaborate the wide range of proposed methods within this scheme. For each functional block, we describe the possible implementations suggested and analyze their underlying assumptions. While impressive advancements were demonstrated at limited scenarios, inspection into the needs of next generation systems reveals significant gaps. We identify these gaps and suggest research directions that may bridge them.

Recent progress in road and lane detection: a survey

Vision-based algorithms for locating lane boundaries without a prior model of the road being viewed need to be able to operate robustly under a wide variety of conditions, including cases where there are large amounts of clutter in the image. This clutter can be due to shadows, puddles, oil stains, tire skid marks, etc. This poses a challenge for edge-based lane detection schemes, as it is often impossible to select a gradient magnitude threshold which doesn't either remove edges of interest corresponding to road markings and edges or include edges corresponding to irrelevant clutter. The approach taken in this work is to use a deformable template model of lane structure to locate lane boundaries without thresholding the intensity gradient information. The Metropolis algorithm is used to maximize a function which evaluates how well the image gradient data supports a given set of template deformation parameters. The result, the LOIS lane detection algorithm (likelihood of image shape), is able to detect lane markings in situations with strong mottled shadows and broken or interrupted lane markings which would pose problems for algorithms which use local, thresholded edge information.

A deformable-template approach to lane detection

The ARCADE (Automated Road Curvature And Direction Estimation) algorithm estimates road curvature and tangential road orientation relative to the camera line-of-sight. The input to ARCADE consists of edge point locations and orientations extracted from an image, and it performs the estimation without the need for any prior perceptual grouping of the edge points into individual lane boundaries. It is able to achieve this through the use of global constraints on the individual lane boundary shapes derived from an explicit model of road structure in the world. The use of the least median squares robust estimation technique allows the algorithm to function correctly in cases where up to 50% of the input edge data points are contaminating noise. Two applications of ARCADE as the first stage of processing for a lane sensing task are described: 1) the extraction of the locations of the features defining the visible lane structure of the road; and 2) the generation of training instances for an ALVINN-like neural network road follower.

Extracting road curvature and orientation from image edge points without perceptual grouping into features

This paper examines intelligent lane tracking in which lane locations can be effectively found in still images. It describes a driver warning system which uses the Likelihood of Image Shape (LOIS) lane detection algorithm to track lanes through a sequence of images and provides a warning in case of imminent lane crossing.

A driver warning system based on the lois lane detection algorithm

This paper describes an algorithm for detecting roads and obstacles in radar data taken from a millimeter-wave imaging platform mounted on a stationary automobile. Such an algorithm is useful in a system that provides all-weather driving assistance. Road boundaries are detected first. The prior shape of the road boundaries is modeled as a deformable template that describes the road edges in terms of its curvature, orientation, and offset. This template is matched to the underlying gradient field of the radar data using a new criterion. The Metropolis algorithm is used to deform the template so that it "best" matches the underlying gradient field. Obstacles are detected next. The radar returns from image pixels that are identified as being part of the road are processed again, and their power levels are compared to a threshold. Pixels belonging to the road that return a significant (greater than a fixed threshold) amount of incident radar power are identified as potential obstacles. The performance of the algorithm on a large all-weather data set is documented. The road edges and obstacles detected are consistently close to ground truth over the entire data set. A new method for computing the gradient field of radar data is also reported, along with an exposition of the millimeter-wave radar imaging process from a signal-processing perspective.

An algorithm for detecting roads and obstacles in radar images

The growth in the number of vision-based lane sensing algorithms in the literature has far outpaced the development of methods for characterizing the limits of performance of such algorithms. The large amounts of data such systems will need to correctly process to be sufficiently reliable for commercial deployment requires that tools be developed for the evaluation process which are almost completely automated. In order to gain some insight into the issues involved, a small pilot study was performed to compare the reliability of two methods used by the YARF road tracking system for locating white painted stripes in small image windows. The evaluation methodology and results are described. In addition, several proposed metrics for measuring road detectability are defined and evaluated.

Karl C. Kluge

Papers

A deformable-template approach to lane detection

Extracting road curvature and orientation from image edge points without perceptual grouping into features

A driver warning system based on the lois lane detection algorithm

An algorithm for detecting roads and obstacles in radar images

Performance evaluation of vision-based lane sensing: some preliminary tools, metrics, and results