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Yield potential was predicted and mapped for three corn fields in Central Illinois, using digital aerial color infrared images. Three methods, namely statistical (regression) modeling, genetic algorithm optimization and artificial neural networks, were used for developing yield models. Two image resolutions of 3 and 6 m/pixel were used for modeling. All the models were trained using July 31 image and tested using images from July 2 and August 31, all from 1998. Among the three models, artificial neural networks gave best performance, with a prediction error less than 30%. The statistical model resulted in prediction errors in the range of 23 to 54%. The lower resolution images resulted in better prediction accuracy compared to resolutions higher than or equal to the yield resolution. Images after pollination resulted in better accuracy compared to images before pollination.

Data obtained when harvesting with a combine equipped with a yield monitor were used to develop yield maps. A prototype yield monitor was developed that uses a combination of light emitters and receivers mounted in a rectangular frame. The monitor was mounted in the combine in the top of the clean grain elevator. As grain flows through the monitor, a voltage change proportional to light reduction was recorded. This voltage was then correlated to grain flow rate. At the same time, site-specific location was recorded using the global positioning satellites (GPS) system. The location data, yield monitor output, cutting width, and combine forward speed were stored in a spreadsheet format. The data were then used to prepare the yield maps.

With COSMOsim, OTSL is looking to better virtually recreate diverse driving conditions to enable safe and accurate verifications at a point when autonomous driving continues to move toward practical use around the world.

Through this work, Wind River and Airbiquity look to enable secure and intelligent software updates and data management for these vehicles through over-the-air (OTA) programming technology. The work may also lead to similar solutions for traditional aerospace and unmanned aircraft system (UAS) industries.

September 17-19, 2019 │ Garden Grove, CA, USA

September 17-19, 2019 │ Garden Grove, CA, USA

This SAE standard presents the basic information required for the design and manufacture of a wheel chock.

The environmental impact from herbicide utilization has been well documented in recent years. The reduction in weed control with out a viable alternative will likely result in decreased per acre production and thus higher unit production cost. The potential for selective herbicide application to reduce herbicide usage and yet maintain adequate weed control has generated significant interest in different forms of remote sensing of agricultural crops. This research evaluated the color co-occurrence texture analysis technique to determine its potential for utilization in crop groundcover identification. A program termed GCVIS (Ground Cover VISion) was developed to control an ATT TARGA 24 frame grabber; and generate HSI color features from the RGB format pixel data, HSI CCM matrices and the co-occurrence texture feature data.

The China Automotive Technology and Research Center Co., Ltd. (CATARC), TÜV SÜD Group, and Shanghai SH Intelligent Automotive and International Transportation Innovation Center (ITIC) have joined with SAE International to establish the International Alliance for Mobility Testing and Standardization (IAMTS).

Computer algorithms were developed for generating the guidance parameters necessary to steer an agricultural tractor. A variety of field operations were considered in order that the guidance program be suited for general applications including travel in curved rows and following a single edge. Testing of the guidance algorithm was performed in the laboratory using simulated and videotaped images of rowcrops and tilled soil. From the images, yaw angle change of the tractor, direction value and offset error were computed. Prediction of the direction value and offset error compared well to measured values. Accuracy of the direction value was within +/- 0.5 degrees while the offset error was within +/- 0.05 meters. Good performance was observed for straight and curved rows as well as following a single edge.

The existing instrumentation of a soil bin was retrofitted with virtual instrumentation techniques to achieve improved repeatability and more precise measurements. Current-loop sensors were added to the prime mover for improved speed control. Soil preparation operations were instrumented to determine penetrometer forces as a function of soil penetration depth, soil surface smoothness, compaction force, and soil surface elevation. Test hitch-points for agricultural implements were instrumented with wheatstone bridge force transducers. Implement depth was found with ratiometric linear transducers. Distance and speed determinations utilized an optical encoder with a resolution of 3.0 × 10-4 m. Temperature measurements were also recorded with solid state current transducers.

Vermeer concept targets autonomous baling

Three-dimensional models of real world terrain have application in a variety of tasks, but digitizing a large environment poses constraints on the design of a 3D scanning system. We have developed a Mobile Scanning System that works within these constraints to quickly digitize large-scale real world environments. We utilize a mobile platform to move our sensors past the scene to be digitized - fusing the data from cm-level accuracy laser range scanners, positioning and orientation instruments, and high-resolution video cameras - to provide the mobility and speed required to quickly and accurately model the target scene.

The Distillation Index (DI) is a measure of the volatility of gasoline, especially its tendency to vaporize in an engine at initial start-up and during warm up. On January 27, 1999 the U.S. domestic and import automotive manufacturers petitioned the US EPA to limit the DI of all U.S. gasoline to 1200 degrees Fahrenheit as a means of reducing in-use emissions and ensuring consistent cold start and warm-up driveability.[1] Air Improvement Resource, Inc. (AIR) completed a 1999 study that evaluated the benefits of a DI cap. Overall, the 1999 AIR study estimated that the DI cap would produce a 16 and 15 percent reduction in hydrocarbon (HC) and carbon monoxide (CO) exhaust, respectively, from gasoline vehicles nationally in 2020. [2] In 2000, the Alliance of Automobile Manufacturers sponsored a more compreshensive examination of the emission consequences of the DI cap on which this paper is based.

Several papers have already been published on the subject of failure analysis of rolling bearings. These papers are very useful. However, with the advancement of bearing technology bearings are increasingly being used in more severe applications. As a result, bearings are experiencing types of failures not described in previous publications. This paper focuses on the quick diagnosis of failed rolling bearings using a magnifying glass or microscopy of low magnification with the emphasis on those failures which have not been previously described.

Several GoPro camera models contain Global Positioning System (GPS), accelerometer, and gyroscope instrumentation and are capable of measuring and recording position, velocity, acceleration, and inertial data. This study evaluates the accuracy of data obtained from GoPro cameras through a series of controlled tests. A test vehicle was instrumented with a Racelogic VBOX data acquisition unit as well as various generations of GoPro camera units equipped with GPS capability and driven on a road course. The raw data collected with the GoPro cameras and the translations of this data provided by the GoPro Quik desktop software application were compared to data collected with the validated VBOX data acquisition unit. The results demonstrated that position, velocity, and acceleration data recorded with GoPro cameras is consistent with VBOX data and is useful for applications related to accident reconstruction.

Shadow positions can be useful in determining the time of day that a photograph was taken and determining the position, size, and orientation of an object casting a shadow in a scene. Astronomical equations can predict the location of the sun relative to the earth, and therefore the position of shadows cast by objects, based on the location’s latitude and longitude as well as the date and time. 3D computer software have begun to include these calculations as a part of their built-in sun systems. In this paper, the authors examine the sun system in the 3D modeling software Blender to determine its accuracy for use in accident reconstruction. A parking lot was scanned using Faro LiDAR scanner to create a point cloud of the environment. A camera was then set up on a tripod at the environment and photographs were taken at various times throughout the day from the same location in the environment.

Creating a 3-dimensional environment using imagery from small unmanned aerial systems (sUAS, or unmanned aerial vehicles -UAVs, or colloquially, drones) has grown in popularity recently in accident reconstruction. In this process, ground control points are placed at an accident scene and an sUAS is flown over an accident site and a series of overlapping, high resolution images are taken of the site. Those images and ground control points are then loaded onto a computer and processed using photogrammetric software to create a 3-dimensional point cloud or mesh of the site, which then can be used as a tool for recreating an accident scene. Many software packages have been created to perform these tasks, and in this paper, the authors examine RealityCapture, a newer photogrammetric software, to evaluate its accuracy for the use in accident reconstruction. It is the authors’ experience that RealityCapture may at times produce point clouds with less noise that other software packages.

Valeo debuts new lidars and EV tech at CES 2022

Image segmentation has historically been a technique for analyzing terrain for military autonomous vehicles. One of the weaknesses of image segmentation from camera data is that it lacks depth information, and it can be affected by environment lighting. Light detection and ranging (LiDAR) is an emerging technology in image segmentation that is able to estimate distances to the objects it detects. One advantage of LiDAR is the ability to gather accurate distances regardless of day, night, shadows, or glare. This study examines LiDAR and camera image segmentation fusion to improve an advanced driver-assistance systems (ADAS) algorithm for off-road autonomous military vehicles. The volume of points generated by LiDAR provides the vehicle with distance and spatial data surrounding the vehicle.