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In the 2016 SAE publication “Data Acquisition using Smart Phone Applications,” Neale et al., evaluated the accuracy of basic fitness applications in tracking position and elevation using the GPS and accelerometer technology contained within the smart phone itself [1]. This paper further develops the research by evaluating mid-level applications. Mid-level applications are defined as ones that use a phone’s internal accelerometer and record data at 1 Hz or greater. The application can also utilize add-on devices, such as a Bluetooth enabled GPS antenna, which reports at a higher sample rate (10 Hz) than the phone by itself. These mid-level applications are still relatively easy to use, lightweight and affordable [2], [3], [4], but have the potential for higher data sample rates for the accelerometer (due to the software) and GPS signal (due to the hardware). In this paper, Harry’s Lap Timer™ was evaluated as a smart phone mid-level application.

Computer simulation of nighttime lighting in urban environments can be complex due to the myriad of light sources present (e.g., street lamps, building lights, signage, and vehicle headlamps). In these areas, vehicle headlamps can make a significant contribution to the lighting environment 1 , 2 . This contribution may need to be incorporated into a lighting simulation to accurately calculate overall light levels and to represent how the light affects the experience and quality of the environment. Within a lighting simulation, photometric files, such as the photometric standard light data file format, are often used to simulate light sources such as street lamps and exterior building lights in nighttime environments. This paper examines the validity of using these same photometric file types for the simulation of vehicle headlamps by comparing the light distribution from actual vehicle headlamps to photometric files of these same headlamps.

This paper examines a method for generating a scaled three-dimensional computer model of an accident scene from video footage. This method, which combines the previously published methods of video tracking and camera projection, includes automated mapping of physical evidence through rectification of each frame. Video Tracking is a photogrammetric technique for obtaining three-dimensional data from a scene using video and was described in a 2004 publication titled, “A Video Tracking Photogrammetry Technique to Survey Roadways for Accident Reconstruction” (SAE 2004-01-1221).

The forward lighting systems on a motorcycle differ from the forward lighting systems on passenger cars, trucks, and tractor trailer. Many motorcycles, for instance, have only a single headlamp. For motorcycles that have more than one headlamp, the total width between the headlamps is still significantly less than the width of an automobile, an important component in the detection of a vehicle at night, as well as a factor in the efficacy of the beam pattern to help a driver see ahead. Single headlamp configurations are centered on the vehicle, and provide little assistance in marking the outside boundaries like a passenger car or truck headlamps can. Further, because of the dynamics of a motorcycle, the performance of the headlamp will differ around turns or corners, since the motorcycle must lean in order to negotiate a turn. As a result, the beam pattern, and hence visibility, provided by the headlamps on a motorcycle are unique for motorized vehicles.

This paper presents analyses of 21real-world pedestrian versus vehicle collisions that were video recorded from vehicle dash mounted cameras or surveillance cameras. These pedestrian collisions have in common an impact configuration where the pedestrian was at the side of the vehicle, or with a minimal overlap at the front corner of the vehicle (less than one foot overlap). These impacts would not be considered frontal impacts [1], and as a result determining the speed of the vehicle by existing methods that incorporate the pedestrian travel distance post impact, or by assessing vehicle damage, would not be applicable. This research examined the specific interaction of non-frontal, side-impact, and minimal overlap pedestrian impact configurations to assess the relationship between the speed of the vehicle at impact, the motion of the pedestrian before and after impact, and the associated post impact travel distances.