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Typical everyday driving scenarios involve acceleration ranges which are relevant to accident reconstruction. Understanding the motions and accelerations endured in common driving maneuvers can help quantify the accelerations of vehicles and occupants when reconstructing a collision. This paper evaluates various everyday driving conditions, such as traversing speed bumps and dips, and impacting parking blocks. The purpose of this paper is to quantify the accelerations experienced during everyday driving scenarios to provide a reference for impact severity analysis in the field of accident reconstruction.

Video of an event recorded from a moving camera contains information not only useful for reconstructing the locations and timing of an event, but also the velocity of the camera attached to the moving object or vehicle. Determining the velocity of a video camera recording from a moving vehicle is useful for determining the vehicle’s velocity and can be compared with speeds calculated through other reconstruction methods, or to data from vehicle speed monitoring devices. After tracking the video, the positions and speeds of other objects within the video can also be determined. Video tracking analysis traditionally has required a site inspection to map the three-dimensional environment. In instances where there have been significant site changes, where there is limited or no site access, and where budgeting and timing constraints exist, a three-dimensional environment can be created using publicly available aerial imagery and aerial LiDAR.

Technology is ever advancing in the world around us, and it is no different when it comes to data acquisition systems used in accident reconstruction. In 2016, the SAE publication “Data Acquisition Using Smart Phone Applications,” Neale et al. evaluated the accuracy of basic fitness applications in tracking position within the smart phone itself [1]. In 2018, a follow up publication “Mid-Range Data Acquisition Units Using GPS and Accelerometers” tested the Harry’s Lap TimerTM application for use in smart phones and compared the data to the Race Logic VBOX [2]. In this paper, another data acquisition system, the MoTeC C185, was tested. The MoTeC C185 data logger contains an internal 3-axis accelerometer and was also equipped with an external Syvecs 50Hz GPS Module with 6-axis accelerometer. A test vehicle was instrumented with the MoTeC C185, Race Logic VBOX, and Harry’s Lap TimerTM.

Approaching an intersection and braking to a stop, as well as accelerating from a stop, is a common occurrence in daily life. While the experience is routine, the actual rate of deceleration and acceleration has not been analyzed from an orthogonal aerial perspective. The aerial perspective provides video footage that allows for accurate planar motion tracking and does not influence the drivers’ actions in any way. This paper examines the behavior of drivers at two separate signal light controlled intersections to determine both the rate at which they slow down to a stop, and also the rate at which they accelerate through the intersection after a signal change. The paper will also address the acceleration rate differences of vehicles who are first to reach the intersection in comparison to those that are directly behind another vehicle, as well as the lag in reaction between vehicles as they begin to accelerate from a stop.

Accidents involving heavy trucks turning left across travel lanes of a roadway are common subjects of investigation in the field of accident reconstruction. The distance traversed during a turn and lateral and tangential accelerations of the left turning heavy truck can be used to model its motion and determine timing as it relates to a collision. As a follow up to the 2019 SAE Accident Reconstruction section paper by the authors (2019-01-0411), this paper will investigate the longitudinal and lateral accelerations of heavy trucks during small, medium, and large radius turns and analyze peak and average lateral accelerations as they relate to turn radius and vehicle speeds. This study analyzed 70 tractor-trailers, 19 straight trucks and 15 bobtail tractors for a total of 104 heavy trucks.

In low speed collisions (under 15 mph) that involve a heavy truck impacting the rear of a passenger vehicle, it is likely that the front bumper of the heavy truck will override the rear bumper beam of the passenger vehicle, creating an override/underride impact configuration. There is limited data available for study when attempting to quantify vehicle damage and crash dynamics in low-speed override/underride impacts. Low speed impact tests were conducted to provide new data for passenger vehicle dynamics and damage assessment for low speed override/underride rear impacts to passenger vehicles. Three tests were conducted, with a tractor-trailer impacting three different passenger vehicles at 5 mph and 10 mph. This paper presents data from these three tests in order to expand the available data set for low speed override/underride collisions.

There have been several papers published over the past 25 years regarding the acceleration of heavy trucks, including different loading conditions, drivetrain configurations, and driving techniques. The papers provide a large data set that measures the speed, distance, and time of the vehicles during acceleration testing and present the data in tabular or graphical formats. Although the data as presented can be useful, it can be challenging to pore over all the data to determine the correct set for a specific application in accident reconstruction. As of this paper’s date of publication, there are approximately eight relevant papers with a total of 268 acceleration tests performed, spanning many years. This paper reviews all the available published literature and summarizes the relevant data in a comprehensive list of accelerations for different heavy truck configurations, which provides a valuable resource to the accident reconstruction field.

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.

The data obtained from event data recorders found in airbag control modules, powertrain control modules and rollover sensors in passenger vehicles has been validated and used to reconstruct crashes for years. Recently, a third-party system has been introduced that allows crash investigators and reconstructionists to access, preserve and analyze data from infotainment and telematics systems found in passenger vehicles. The infotainment and telematics systems in select vehicles retain information and event data from cellular telephones and other devices connected to the vehicle, vehicle events and navigation data in the form of tracklogs. These tracklogs provide a time history of a vehicle’s geolocation that may be useful in investigating an incident involving an automobile or reconstructing a crash. This paper presents an introduction to the type of data that may be retained and the methods for performing data acquisitions.

There are numerous publically available smart phone applications designed to track the speed and position of the user. By accessing the phones built in GPS receivers, these applications record the position over time of the phone and report the record on the phone itself, and typically on the application’s website. These applications range in cost from free to a few dollars, with some, that advertise greater functionality, costing significantly higher. This paper examines the reliability of the data reported through these applications, and the potential for these applications to be useful in certain conditions where monitoring and recording vehicle or pedestrian movement is needed. To analyze the reliability of the applications, three of the more popular and widely used tracking programs were downloaded to three different smart phones to represent a good spectrum of operating platforms.