Vehicle swept path analysis based on GPS data

Abstract

Vehicle swept path analysis presents an essential step while working on at-grade intersection and roundabout designs. Following the intensive development of computer-aided design (CAD) software in the past two decades, numerous CAD-based computer programs for vehicle movement simulation have been developed and commercially distributed. The accuracy of these simulation programs is usually verified by conducting experimental field tests in which real movement trajectories of design vehicles, equipped with global positioning system (GPS) receivers, are recorded. This paper proposes an improved methodology for retrieving vehicle movement trajectories from collected GPS data. The proposed methodology reduces the trajectory inaccuracy resulting from pavement grading characteristics and the inability to accurately install GPS receivers in relation to streamlined vehicle body. Results of field experiments show that the reduction of positioning errors in the horizontal projection is not smaller than 50.0 mm compared with previous studies.

Exact Mathematical Solution for Maximum Transient Offtracking Calculation of a Single-Unit Vehicle Negotiating Circular Curves

“…The method relies on the deployment of an "equivalent base", acting as a virtual base of an entire multibody vehicle assembly. Woodrooffe's procedure retrieves the maximum transient offtracking value, but with the error of 0.06-0.10 m in lateral terms and more than 1.2 m in longitudinal terms, as proven in [26]. On the other hand, commercially available software tools for vehicle swept path analysis based on CAD platform require from the user to draw guideline first, and then based on its shape and generated swept path width, determine the maximum transient offtracking for a selected design vehicle [31].…”

“…Additionally, commercial computer programs for the simulation of vehicle movements and vehicle swept path analysis, such as the Auto CAD Vehicle Tracking 2024 and Transoft AutoTURN 12.0.0, have become indispensable tools for civil engineers and other specialists involved in intersection and roundabout design [24,25]. Recently, extensive analyses, addressing the accuracy of the available numerical models and commercial software applications for setting vehicle movement trajectories, were conducted in a Ph.D. thesis [26]. However, amongst all of the addressed mathematical and numerical models, only a few of them offered a solution for the calculation of the exact vehicle position where the maximum active transient offtracking is reached [7,27].…”

“…Nowadays, as demonstrated in [26], their efficiency and accuracy are not enough for road infrastructure design purposes, especially for autonomous vehicle positioning in horizontal curves. For example, the results of the maximum transient offtracking calculation in Lawrence's mathematical model for low-speed offtracking [27] are generated by the computer in a form of a graph.…”

Fault Recovery Path Analysis of a Software Dynamic Image Based on a Fuzzy Control Algorithm

“…According to the wavelet detector designed in Figure 1, signal denoising can be carried out. The dynamic image fault signal of broadband high-resolution software is set as a set of stationary random signals [21][22][23], which is represented as the multilayer wavelet scale feature decomposition of the signal by ( )…”

“…The software dynamic image fault detection algorithm was optimized on the basis of using embedded feature extraction and fuzzy control algorithm to construct software dynamic image fault signal model, as well as spectrum decomposition and blind source separation processing of According to the wavelet detector designed in Figure 1, signal denoising can be carried out. The dynamic image fault signal of broadband high-resolution software is set as a set of stationary random signals [21][22][23], which is represented as the multilayer wavelet scale feature decomposition of the signal by x(t), the joint parameter estimation of time and frequency of x(t), and the acquisition of discrete signal F s = 1/T s . The fault code width of software dynamic image fault detection in embedded software is T = (2d + 1)T s .…”