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  1. Vehicles can easily lose control unexpectedly when encountering unforeseen hazardous road friction conditions. With automation and connectivity increasingly available to assist drivers, vehicle performance can significantly benefit from a road friction preview map, particularly to identify where and how friction ahead of a vehicle may be suddenly decreasing. Although many techniques enable the vehicle to measure the local friction as driving upon a surface, these encounters limit the ability of a vehicle to slow down before a low-friction surface is already encountered. Using the connectivity of connected and autonomous vehicles (CAVs), a global road friction map can be created by aggregating information from vehicles. A challenge in the creation of these global friction maps is the very large quantity of data involved, and that the measurements populating the map are generated by vehicle trajectories that do not uniformly cover the grid. This paper presents a road friction map generation strategy that aggregates the measured road-tire friction coefficients along the individual trajectories of CAVs into a road surface grid. In addition, through clustering the friction grids further, an insight of this work is that the friction map can be represented compactly by rectangular boxes defined by a pair of corner coordinates in space, a friction value, and a confidence interval within the box. To demonstrate the method, a simulation is presented that integrates traffic simulations, vehicle dynamics and on-vehicle friction estimators, and a highway road surface, where friction is changing in space, particularly over a bridge segment. The experimental results indicate that the road friction distribution can be measured effectively by collecting and aggregating the friction data from CAVs. 
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    Free, publicly-accessible full text available August 1, 2024
  2. Fromme, Paul ; Su, Zhongqing (Ed.)
    Stereovision systems can extract full-field three-dimensional (3D) displacements of structures by processing the images collected with two synchronized cameras. To obtain accurate measurements, the cameras must be calibrated to account for lens distortion (i.e., intrinsic parameters) and compute the cameras’ relative position and orientation (i.e., extrinsic parameters). Traditionally, calibration is performed by taking photos of a calibration object (e.g., a checkerboard) with the two cameras. Because the calibration object must be similar in size to the targeted structure, measurements on large-scale structures are highly impractical. This research proposes a multi-sensor board with three inertial measurement units and a laser distance meter to compute the extrinsic parameters of a stereovision system and streamline the calibration procedure. In this paper, the performances of the proposed sensor-based calibration are compared with the accuracy of the traditional image-based calibration procedure. Laboratory experiments show that cameras calibrated with the multi-sensor board measure displacements with 95% accuracy compared to displacements obtained from cameras calibrated with the traditional procedure. The results of this study indicate that the sensor-based approach can increase the applicability of 3D digital image correlation measurements to large-scale structures while reducing the time and complexity of the calibration. 
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    Free, publicly-accessible full text available April 25, 2024
  3. Moving averages are widely used to estimate time-varying parameters, especially when the underlying dynamic model is unknown or uncertain. However, the selection of the optimal window length over which to evaluate the moving averages remains an unresolved issue in the field. In this paper, we demonstrate the use of Allan variance to identify the characteristic timescales of a noisy random walk from historical measurements. Further, we provide a closed-form, analytical result to show that the Allan variance-informed averaging window length is indeed the optimal averaging window length in the context of moving average estimation of noisy random walks. We complement the analytical proof with numerical results that support the solution, which is also reflected in the authors’ related works. This systematic methodology for selecting the optimal averaging window length using Allan variance is expected to widely benefit practitioners in a diverse array of fields that utilize the moving average estimation technique for noisy random walk signals. 
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  4. Vehicles are highly likely to lose control unexpectedly when encountering unforeseen hazardous road friction conditions. With automation and connectivity increasingly available to assist drivers, vehicle performance can significantly benefit from a road friction preview map, particularly to identify where and how friction ahead of a vehicle may be suddenly decreasing. Although many techniques enable the vehicle to measure the local friction as driving upon a surface, these encounters limit the ability of a vehicle to slow down before a low-friction surface is already encountered. Using the connectivity of connected and autonomous vehicles (CAVs), a global road friction map can be created by aggregating information from vehicles. A challenge in the creation of these global friction maps is the very large quantity of data involved, and that the measurements populating the map are generated by vehicle trajectories that do not uniformly cover the grid. This paper presents a road friction map generation strategy that aggregates the measured road-tire friction coefficients along the individual trajectories of CAVs into a road surface grid. And through clustering the friction grids further, an insight of this work is that the friction map can be represented compactly by rectangular boxes defined by a pair of corner coordinates in space and a friction value within the box. To demonstrate the method, a simulation is presented that integrates traffic simulations, vehicle dynamics and on-vehicle friction estimators, and a highway road surface where friction is changing in space, particularly over a bridge segment. The experimental results indicate that the road friction distribution can be measured effectively by collecting and aggregating the friction data from CAVs. By defining a cloud-based data sharing method for the networks of CAVs, this road friction mapping strategy provides great potential for improving CAVs' control performance and stability via database-mediated feedback systems. 
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  5. Fromme, Paul ; Su, Zhongqing (Ed.)
    Three-dimensional digital image correlation (3D-DIC) has become a strong alternative to traditional contact-based techniques for structural health monitoring. 3D-DIC can extract the full-field displacement of a structure from a set of synchronized stereo images. Before performing 3D-DIC, a complex calibration process must be completed to obtain the stereovision system’s extrinsic parameters (i.e., cameras’ distance and orientation). The time required for the calibration depends on the dimensions of the targeted structure. For example, for large-scale structures, the calibration may take several hours. Furthermore, every time the cameras’ position changes, a new calibration is required to recalculate the extrinsic parameters. The approach proposed in this research allows determining the 3D-DIC extrinsic parameters using the data measured with commercially available sensors. The system utilizes three Inertial Measurement Units with a laser distance meter to compute the relative orientation and distance between the cameras. In this paper, an evaluation of the sensitivity of the newly developed sensor suite is provided by assessing the errors in the measurement of the extrinsic parameters. Analytical simulations performed on a 7.5 x 5.7 m field of view using the data retrieved from the sensors show that the proposed approach provides an accuracy of ~10-6 m and a promising way to reduce the complexity of 3D-DIC calibration. 
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  6. null (Ed.)