A Generalized Framework for Parallelizing Traffic Sign Inventory of Video Log Images Using Multicore Processors

Computer aided Civil Eng

et al. 2017

Crack is one of the most important pavement condition indicators that are immediately relevant to water ingress and pavement deterioration. In practices of pavement management, crack width has been extensively referenced by highway agencies to determine pavement crack severity. Accurate measurement of pavement crack width is meaningful for highway agencies in understanding the mechanism of crack formation, and in predicting crack propagation. This article presents a new automatic method for measuring crack width using the binary crack map images. The proposed method introduces a new crack width definition and formulates it using the Laplace's Equation so that crack width can be continuously and unambiguously measured. Two algorithms, including the crack blob extraction algorithm and the crack

Section: Experimental Testsmentioning

confidence: 99%

Pavement Crack Width Measurement Based on Laplace's Equation for Continuity and Unambiguity

Computer aided Civil Eng

et al. 2017

“…In addition, the algorithm has potential in parallel processing, because a hierarchical domain partition is hidden in the placement of streamlines. Some parallel strategies [18] based on data division [3,9] can be extended to our algorithm. Therefore, our multiresolution analysis of streamlines for both 2D and 3D fields has an advantage in performance.…”

Section: Evaluation Of the Basic Placement Algorithmmentioning

confidence: 99%

Multiresolution streamline placement based on control grids

Ning

et al. 2014

ICA

Multiresolution streamline placement is useful for interactive visualization of flow fields. In this paper, a new method is proposed to build placements of streamlines in different resolutions for 2D and 3D flow fields. This method is developed from a basic streamline placement algorithm that takes control grids to place streamlines in 2D flow fields. The process of multiresolution analysis starts from a sparse placement, which is obtained with a coarse grid according to the basic placement algorithm. The sparse placement is then iteratively refined by elongating all existing streamlines and adding new streamlines under the control of a finer grid. By this means, a series of placements can be built in different resolutions, each of which reuses its previously placed streamlines at increasing density levels. This multiresolution placement for 2D flow fields is further extended to 3D flow fields by changing the basic placement algorithm to fit 3D space. Test results show that the basic streamline placement algorithm is very fast, so the proposed multiresolution analysis can be done for both 2D and 3D flow fields efficiently.

“…To this end, ITS systems integrate information and communication technologies into both the vehicles [4,11,39,43] and the infrastructure [2,[7][8][9][10]22,24,26,42]. The AUTOPIA research group's contributions focus on the development of optimal solutions for present and future transportation challenges [29].…”

Section: Introductionmentioning

confidence: 99%

A transferable belief model applied to LIDAR perception for autonomous vehicles

Domínguez

Alonso

Onieva

et al. 2013

ICA

Light Detection and Ranging (LIDAR) sensors are commonly used in perception for autonomous vehicles because of their high accuracy, speed, and range. These characteristics make the sensor suitable for integration into the perception layer of controllers which have the capacity to avoid collisions with unpredicted obstacles. The objective of this work was to design a robust and efficient algorithm to acquire useful knowledge from LIDAR scans, and to test its performance in real road situations. The method is based on the construction of an Occupancy Grid based on the Transferable Belief Model, which has proved promising in other studies. Two clear advantages of this method are the reduction of the problem complexity in the phases of segmentation, occlusion detection and tracking, and its ease of integration with other sensors to allow the overall system to evolve towards reliable and complete perception.