Snake energy analysis and result validation for a mobile laser scanning data-based automated road edge extraction algorithm

© 2008-2012 IEEE. The negative impact of road accidents cannot be ignored in terms of the very sizeable social and economic loss. Road infrastructure has been identified as one of the main causes of the road accidents. They are required to be recorded, located, measured, and classified in order to schedule maintenance and identify the possible risk elements of the road. Toward this, an accurate knowledge of the road edges increases the reliability and precision of extracting other road features. We have developed an automated algorithm for extracting road edges from mobile laser scanning (MLS) data based on the parametric active contour or snake model. The algorithm involves several internal and external energy parameters that need to be analyzed in order to find their optimal values. In this paper, we present a detailed analysis of the snake energy parameters involved in our road edge extraction algorithm. Their optimal values enable us to automate the process of extracting edges from MLS data for tested road sections. We present a modified external energy in our algorithm and demonstrate its utility for extracting road edges from low and nonuniform point density datasets. A novel validation approach is presented, which provides a qualitative assessment of the extracted road edges based on direct comparisons with reference road edges. This approach provides an alternative to traditional road edge validation methodologies that are based on creating buffer zones around reference road edges and then computing quality measure values for the extracted edges. We tested our road edge extraction algorithm on datasets that were acquired using multiple MLS systems along various complex road sections. The successful extraction of road edges from these datasets validates the robustness of our algorithm for use in complex route corridor environments

An fMRI study of parietal cortex involvement in the visual guidance of locomotion

Locomoting through the environment typically involves anticipating impending changes in heading trajectory in addition to maintaining the current direction of travel. We explored the neural systems involved in the “far road” and “near road” mechanisms proposed by Land and Horwood (1995) using simulated forward or backward travel where participants were required to gauge their current direction of travel (rather than directly control it). During forward egomotion, the distant road edges provided future path information, which participants used to improve their heading judgments. During backward egomotion, the road edges did not enhance performance because they no longer provided prospective information. This behavioral dissociation was reflected at the neural level, where only simulated forward travel increased activation in a region of the superior parietal lobe and the medial intraparietal sulcus. Providing only near road information during a forward heading judgment task resulted in activation in the motion complex. We propose a complementary role for the posterior parietal cortex and motion complex in detecting future path information and maintaining current lane positioning, respectively. (PsycINFO Database Record (c) 2010 APA, all rights reserved

CiNCT: Compression and retrieval for massive vehicular trajectories via relative movement labeling

In this paper, we present a compressed data structure for moving object trajectories in a road network, which are represented as sequences of road edges. Unlike existing compression methods for trajectories in a network, our method supports pattern matching and decompression from an arbitrary position while retaining a high compressibility with theoretical guarantees. Specifically, our method is based on FM-index, a fast and compact data structure for pattern matching. To enhance the compression, we incorporate the sparsity of road networks into the data structure. In particular, we present the novel concepts of relative movement labeling and PseudoRank, each contributing to significant reductions in data size and query processing time. Our theoretical analysis and experimental studies reveal the advantages of our proposed method as compared to existing trajectory compression methods and FM-index variants

Contrast Between Road and Roadside Material For Road Edge Detection In Vehicle Road Departure Mitigation System

Vehicle roadway departure crashes results in a large number of fatalities in the U.S. Road departure mitigation (RDM) systems rely on the road edge and road boundary identification. Cameras are widely used in RDMS for identifying road edges. The contrast between road and road boundary objects is one of the key image features used by the camera to detect road edges. This paper analyzes and compares the contrasts between various road surfaces. and road edges

Controlling Steering Using Vision

The Two-level model is a popular account of how humans use visual information to successfully control steering within road edges. A guidance component uses information from far regions to preview upcoming steering requirements, and a compensatory component uses information from near regions to stabilise position-in-lane. Researchers who have considered the case of driving often treat road edges as the sole informational input for controlling steering, but this approach is not consistent with the notion that the human visual system adaptively uses multiple inputs to maintain robust control of steering. A rich source of information which may also be useful for steering control is optic flow. Chapter 2 demonstrates that optic flow speed is used to control steering even with road edges present. Chapters 3-5 develop a framework to examine how use of flow speed changes depending on the availability of guidance or compensatory road edge information, and demonstrate that use of flow speed increases only when guidance level information (far road edges) is present. Chapters 6-7 go on to examine the contribution of flow direction to controlling steering within road edges, and demonstrate that the use of flow direction appears to be yoked to the presence of compensatory information (near road edges). Together, these experiments demonstrate that the contribution of flow information to controlling steering within road edges can be understood within the context of two-level steering, and show that an approach which emphasise robust control through combining multiple informational inputs is vital if we are to fully understand how the visual-motor system solves the problem of steering along constrained path

Road Edge Extraction Using a Plan-View Image Transformation

A new technique to extract road edges in the road-following algorithm for autonomous road vehicle navi-gation is described. It is based on finding road edges on a subsampled plan-view of a portion of the road ahead of the vehicle. The method is illustrated in the real-time identification of road edges using a fast vertical edge de-tector and link operator applied to the transformed plan view. Location of both road edges at 20 frames per sec-ond is demonstrated. Research on autonomous navigation of robot vehicles has been increasing in the last few years 1'2>3. Part of this research consists of identifying the road from a digitised image received by a camera positioned on the robot ve-hicle. The problem of identifying roads to drive an autonomou

The role of gaze and road edge information during high speed locomotion.

Robust control of skilled actions requires the flexible combination of multiple sources of information. Here we examined the role of gaze during high-speed locomotor steering and in particular the role of feedback from the visible road edges. Participants were required to maintain one of three lateral positions on the road when one or both edges were degraded (either by fading or removing them). Steering became increasingly impaired as road edge information was degraded, with gaze being predominantly directed towards the required road position. When either of the road edges were removed, we observed systematic shifts in steering and gaze direction dependent upon both the required road position and the visible edge. A second experiment required fixation on the road center or beyond the road edges. The results showed that the direction of gaze led to predictable steering biases, which increased as road edge information became degraded. A new steering model demonstrates that the direction of gaze and both road edges influence steering in a manner consistent with the flexible weighted combination of near road feedback information and prospective gaze information

The Potential of Active Contour Models in Extracting Road Edges from Mobile Laser Scanning Data

Active contour models present a robust segmentation approach, which makes efficient use of specific information about objects in the input data rather than processing all of the data. They have been widely-used in many applications, including image segmentation, object boundary localisation, motion tracking, shape modelling, stereo matching and object reconstruction. In this paper, we investigate the potential of active contour models in extracting road edges from Mobile Laser Scanning (MLS) data. The categorisation of active contours based on their mathematical representation and implementation is discussed in detail. We discuss an integrated version in which active contour models are combined to overcome their limitations. We review various active contour-based methodologies, which have been developed to extract road features from LiDAR and digital imaging datasets. We present a case study in which an integrated version of active contour models is applied to extract road edges from MLS dataset. An accurate extraction of left and right edges from the tested road section validates the use of active contour models. The present study provides valuable insight into the potential of active contours for extracting roads from 3D LiDAR point cloud data

Salamander Abundance along Road Edges and within Abandoned Logging Roads in Appalachian Forests

Roads may be one of the most common disturbances in otherwise continuous forested habitat in the southern Appalachian Mountains. Despite their obvious presence on the landscape, there is limited data on the ecological effects along a road edge or the size of the “road-effect zone.” We sampled salamanders at current and abandoned road sites within the Nantahala National Forest, North Carolina (U.S.A.) to determine the road-effect zone for an assemblage of woodland salamanders. Salamander abundance near the road was reduced significantly, and salamanders along the edges were predominantly large individuals. These results indicate that the road-effect zone for these salamanders extended 35 m on either side of the relatively narrow, low-use forest roads along which we sampled. Furthermore, salamander abundance was significantly lower on old, abandoned logging roads compared with the adjacent upslope sites. These results indicate that forest roads and abandoned logging roads have negative effects on forest-dependent species such as plethodontid salamanders. Our results may apply to other protected forests in the southern Appalachians and may exemplify a problem created by current and past land use activities in all forested regions, especially those related to road building for natural-resource extraction. Our results show that the effect of roads reached well beyond their boundary and that abandonment or the decommissioning of roads did not reverse detrimental ecological effects; rather, our results indicate that management decisions have significant repercussions for generations to come. Furthermore, the quantity of suitable forested habitat in the protected areas we studied was significantly reduced: between 28.6% and 36.9% of the area was affected by roads. Management and policy decisions must use current and historical data on land use to understand cumulative impacts on forest-dependent species and to fully protect biodiversity on national landsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73456/1/j.1523-1739.2006.00571.x.pd

Challenges in Partially-Automated Roadway Feature Mapping Using Mobile Laser Scanning and Vehicle Trajectory Data

Connected vehicle and driver's assistance applications are greatly facilitated by Enhanced Digital Maps (EDMs) that represent roadway features (e.g., lane edges or centerlines, stop bars). Due to the large number of signalized intersections and miles of roadway, manual development of EDMs on a global basis is not feasible. Mobile Terrestrial Laser Scanning (MTLS) is the preferred data acquisition method to provide data for automated EDM development. Such systems provide an MTLS trajectory and a point cloud for the roadway environment. The challenge is to automatically convert these data into an EDM. This article presents a new processing and feature extraction method, experimental demonstration providing SAE-J2735 map messages for eleven example intersections, and a discussion of the results that points out remaining challenges and suggests directions for future research.Comment: 6 pages, 5 figure