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The in-cylinder bulk flow and turbulence characteristics in a spark ignition engine were examined using two particle image velocimetry (PIV) systems. The effects of a turbulence-generating valve (TGV) bulk flow and turbulence characteristics were investigated. The results show that both the motion and location of turbulent flow can be controlled by a TGV valve. This could be used to reduce the level of unburned hydrocarbons produced during a cold start. The time history and scales of turbulence [1, 2 and 3] were compared with those obtained using laser Doppler velocimetry. The developed PIV systems were accurate, and the measured data were reliable enough to permit discussion of the cycle-resolve and cycle-to-cycle variation of in-cylinder flow. At top dead center, the measured turbulence scales of motion in the flow with the TGV closed were two thirds of those obtained with the TGV open.

Crash information, e.g. driving and impact speed, have to be determined from traces on the scene, as well as from examination of deformation patterns in order to assess the impact condition and the movement trajectories of the impacted body of bicyclists and pedestrians after car collision. Experts use the following information to calculate speed: Information on final position of vehicles, deformation pattern on vehicles, traces found on the road, such as braking and sliding marks, throw distances of pedestrians and cyclists and injury pattern, all these issues are given possibilities for reconstruction of the movement of the human body. While in car to car crashes the speed calculation is based on the momentum analysis and on energy balance hypothesis of classical physics, the calculation for pedestrian and bicycle accidents have to be based on traces only. The paper describes the possibilities of the use of throw distance as a reconstruction method.

Low-speed sideswipe collisions between tractor-semitrailers and passenger vehicles may result in large areas of visible damage to the passenger vehicle. However, due to the extended contact that occurs during these impacts, it is typical in these incidents for the crash pulse duration to be long and the vehicle accelerations to be correspondingly low. Research regarding the impact environment and resulting injury potential of the occupants during these types of impacts is limited. Five full-scale crash tests utilizing a tractor-semitrailer and a passenger car were conducted to explore the occupant responses during these types of collisions. The test vehicles included a van semitrailer pulled by a tractor and three identical mid-sized sedans. The occupants of the sedans included an instrumented Hybrid III 5th -percentile-male anthropomorphic test device (ATD) in the driver's seat and an un-instrumented Hybrid III 5th -percentile-female ATD in the left rear seat.

Recent models of General Motors (GM) and selected Ford vehicles may be equipped with an event data recorder (EDR) that records information in the airbag sensing and diagnostic module (GM-SDM) or restraint control module (Ford-RCM). These systems have become a resource to the accident reconstructionist in the analysis of collisions involving data recorder equipped vehicles, as typically the data can be downloaded via the Vetronix Crash Data Retrieval (CDR) System. The purpose of this paper is to investigate the use of the CDR System in pedestrian accidents. A series of impacts using a pedestrian dummy and SDM equipped vehicles were performed. After each test, the SDM was downloaded via the CDR system and the data evaluated. The dummy and vehicle kinematics were documented and the vehicle impact response was compared with the SDM recorded velocity change and impact speed.

Collision data stored in the airbag sensing and diagnostic module (SDM) of 1996 and newer GM vehicles have become available to accident investigators through the Vetronix Crash Data Retrieval system. In this study, two experiments were performed to investigate the accuracy and sensitivity of the speed change reported by the SDM in low-speed crashes. First, two SDM-equipped vehicles were subjected to 260 staged frontal collisions with speed changes below 11 km/h. Second, the SDMs were removed from the vehicles and exposed to a wide variety of collision pulses on a linear motion sled. In all of the vehicle tests, the speed change reported by the SDM underestimated the actual speed change of the vehicle. Sled testing revealed that the shape, duration and peak acceleration of the collision pulse affected the accuracy of the SDM-reported speed change. Data from the sled tests were then used to evaluate how the SDM-reported speed change was calculated.

Autonomous driving is a hot topic in the automotive domain, and there is an increasing need to prove its reliability. They use machine learning techniques, which are themselves stochastic techniques based on some kind of statistical inference. The occurrence of incorrect decisions is part of this approach and often not directly related to correctable errors. The quality of the systems is indicated by statistical key figures such as accuracy and precision. Numerous driving tests and simulations in simulators are extensively used to provide evidence. However, the basis of all descriptive statistics is a random selection from a probability space. The difficulty in testing or constructing the training and test data set is that this probability space is usually not well defined. To systematically address this shortcoming, ontologies have been and are being developed to capture the various concepts and properties of the operational design domain.

A number of methods have been presented previously in the literature for determination of the impact speed of a motorcycle or scooter at its point of contact with another, typically larger and heavier, vehicle or object. However, all introduced methods to date have known limitations, especially as there are often significant challenges in gathering the needed data after a collision. Unlike passenger vehicles and commercial vehicles, most motorcycles and scooters carry no onboard electronic data recorders to provide insight into the impact phase of the collision. Recent research into automobile speedometers has shown that certain types of modern stepper motor based speedometers and tachometers can provide useful data for a collision reconstruction analysis if the instrument cluster loses electrical power during the impact, resulting in a “frozen” needle indication.

Volunteer subject studies in low-speed rear impacts have shown that significant lumbar spine injuries are unlikely in such collisions. Anthropomorphic test devices (ATD) used in low to medium speed rear impact simulations have similarly revealed an unlikely mechanism to cause lumbar spine injuries. However, low back complaints after rear impacts are common in clinical practice. We attempt here to determine the incidence of lumbar spine injuries from actual field data which may provide an insight into the apparent paradox between experimental data and clinical practice. We examined the incidence of all spine injuries in the NASSCDS (National Automotive Sampling System - Crashworthiness Data System) database from 1993 to 2009. We limited the data to only look at rear-end crashes involving two vehicles.

Vehicle post-impact travel distances are often available to the accident reconstructionist. Energy dissipated after impact can be significant, and it is often necessary to account for this energy. The deceleration and energy dissipation experienced by a vehicle after a collision is dependent on many variables including tire rolling resistance, engine and drive-train resistance and aerodynamic drag. New technologies that significantly modify the traditional drive train, low rolling resistance tires, and new aerodynamic body designs affect vehicle deceleration, but associated data is not widely available. Roll-out tests were performed in which speed, acceleration and position measurements were made. Vehicles tested were equipped with hybrid (gasoline-electric) and standard engines, CVT (continuously variable transmission), manual and automatic transmissions, and two wheel and four-wheel drive.

The MacPherson strut is a simple and common across all automotive’s front suspension of passenger cars. It is an independent suspension type, including a single suspension arm (spring and damper), an anti-roll bar and a lower arm. The MacPherson strut must have sufficient stiffness to support cornering force and fore/aft loads. Fatigue test of MacPherson strut suspension can be done in multiple ways. Most common method is laboratory testing/rig test. The objective of laboratory testing is to validate the MacPherson strut physically for all possible real-time events. Replicating all real-time events in lab environment is a challenging task. For many years this limitation was addressed through experience, however it has often led to either over or inferior design. The expected life span of automotive components like MacPherson strut varies considerably but it can be measurable in years/miles.

Instrumented human subject and anthropomorphic test device (ATD) responses to low speed lateral impacts were investigated. A series of 12 lateral collisions at various impact angles were conducted, 6 near-side and 6 far-side, with each test using an ATD and one human subject. Two restrained female subjects were utilized, with one positioned in the driver seat and one in the left rear seat. Each subject was exposed to 3 near-side and 3 far-side impacts. The restrained ATD was utilized in both the driver and left rear seats, undergoing 3 near-side and 3 far-side impacts in each position. The vehicle center of gravity (CG) change in velocity (delta-V) ranged from 5.5 to 9.4 km/h (3.4 to 5.8 mph). Video analysis was used for quantification and comparison of the human and ATD motions and interactions with interior vehicle structures. Human head, thorax, and low back accelerations were analyzed. Peak human subject head resultant accelerations ranged from 0.9 to 36.8 g’s.

Using dashcam video footage to extract reliable vehicle speed data can be challenging when the only available image stream comes from a camera whose optical parameters are unknown. One means of overcoming such difficulties uses visible landmarks and features within the video frame whose dimensions can be independently measured. While good results have been obtained by others using a Total Station or LiDAR to physically measure locations for such purposes, this approach could prove difficult if a site of interest is inaccessible (e.g. on a busy highway that cannot be shut down) or if relevent features of the target location have changed (e.g. due to construction or even restriping of lanes lines). As an alternative to direct scene measurement, it is proposed that measurement of features visible in overhead satellite images be used to dimension relevant features visible in the video.

This paper is the fourth printed listing of the National Highway Traffic Safety Administration’s (NHTSA) center-of-gravity (CG) location measurements. The previous papers contained data for 1024 vehicles. This paper includes data for 448 additional vehicles tested as part of NHTSA’s New Car Assessment Program (NCAP) for the years 2009 through year 2020. The NCAP involves only the CG location measurement; so the vehicles listed in this paper do not have inertial data.

Tire-pavement interaction noise (TPIN) is a dominant source for passenger cars and trucks above 40 km/h and 70 km/h, respectively. TPIN is mainly generated from the interaction between the tire and the pavement. In this paper, twenty-two passenger car radial (PCR) tires of the same size (16 in. radius) but with different tread patterns were tested on a non-porous asphalt pavement. For each tire, the noise data were collected using an on-board sound intensity (OBSI) system at five speeds in the range from 45 to 65 mph (from 72 to 105 km/h). The OBSI system used an optical sensor to record a once-per-revolution signal to monitor the vehicle speed. This signal was also used to perform order tracking analysis to break down the total tire noise into two components: tread pattern-related noise and non-tread pattern-related noise.

In this paper, a control strategy for state of charge (SOC) allocation using navigation data for Hybrid Electric Vehicle (HEV) propulsion systems is proposed. This algorithm dynamically defines and adjusts a SOC target as a function of distance travelled on-line, thereby enabling proactive management of the energy store in the battery. The proposed approach incorporates variances in road resistance and adheres to geolocation constraints, including ultra-low emission zones (uLEZ). The anticipated advantages are particularly pronounced during scenarios involving extensive medium-to-long journeys characterized by abrupt topological changes or the necessity for exclusive electric vehicle (EV) mode operation. This novel solution stands to significantly enhance both drivability and fuel economy outcomes.

This paper focuses on an inherent problems of active damping control prevalent in contemporary hybrid torque controls. Oftentimes, a supervisory torque controller utilizes simplified system models with minimal system states representation within the optimization problem, often not accounting for nonlinearities and stiffness. This is motivated by enabling the generation of the optimum torque commands with minimum computational burden. When inherent lash and stiffness of the driveline are not considered, the resulting command can lead to vibrations and oscillations in the powertrain, reducing performance and comfort. The paper proposes a Linear Quadratic Integral (LQI)-based compensator to be integrated downstream the torque supervisory algorithm, which role is to shape transient electric machine torques, compensating for the stiffness and backlash present in the vehicle while delivering the driver-requested wheel torque.