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Heavy Vehicle Event Data Recorders (HVEDRs) have the ability to capture important data surrounding an event such as a crash or near crash. Efforts by many researchers to analyze the capabilities and performance of these complex systems can be problematic, in part, due to the challenges of obtaining a heavy truck, the necessary space to safely test systems, the inherent unpredictability in testing, and the costs associated with this research. In this paper, a method for simulating vehicle speed sensor (VSS) inputs to HVEDRs to trigger events is introduced and validated. Full-scale instrumented testing is conducted to capture raw VSS signals during steady state and braking conditions. The recorded steady state VSS signals are injected into the HVEDR along with synthesized signals to evaluate the response of the HVEDR. Brake testing VSS signals are similarly captured and injected into the HVEDR to trigger an event record.

Modern heavy vehicles may be equipped with an Advanced Driver Assistance System (ADAS) designed to increase highway safety. Depending on the vehicle or manufacturer, these systems may detect objects in a driver’s blind spot, provide an alert when the ADAS determines that the vehicle is leaving its lane of travel without the use of a turn signal, or notify the driver when certain road signs are detected. ADASs also include adaptive cruise control, which adjusts the vehicle’s set cruise speed to maintain a safe following distance when a slower vehicle is detected ahead of the truck. In addition, the ADAS may have a Collision Mitigation System (CMS) component that is designed to help drivers respond to roadway situations and reduce the severity of crashes. CMSs typically use radar or a combination of radar and optical technologies to detect objects such as vehicles or pedestrians in the vehicle’s path.

The Daimler Detroit AssuranceⓇ 4.0 collision mitigation system is able to assist a driver in various aspects of safely operating their vehicle. One capability is the Active Brake Assist (ABA), which uses the Video Radar Decision Unit (VRDU) to communicate with the front bumper-mounted radar to provide information about potential hazards to the driver. The VRDU may warn the driver of potential hazards and apply partial or full braking, depending on the data being gathered and analyzed. The VRDU also records event data when an ABA event occurs. This data may be extracted from the VRDU using Detroit DiagnosticLink software. This paper presents an overview of the VRDU functionality and examines aspects of VRDU data such as the range and resolution of data elements, the synchronicity or timing of the recorded data, and application of the data for use in the analysis of crashes.

Electronic control units of Bendix® ABS/ESC and Collision Mitigation Systems have the capability to record event data in the ABS/ESC control unit. Bendix refers to this event data recording functionality as the Bendix Data Recorder (BDR). This paper presents an overview of the BDR functionality and examines the range and resolution of data elements, the synchronicity or timing of the recorded data, and application of the data for use in analyzing crashes. Various tests were performed using trucks equipped with Bendix® Wingman® Fusion™ and were conducted in a manner to trigger BDR records. BDR data was compared to data collected from the J1939 CAN Bus and from Racelogic VBOX data loggers.

The time/distance relationship for a motor coach accelerating from a stop is often needed to accurately assess the events leading up to a collision. Several series of tests were conducted to document the low speed acceleration performance of a 45-foot long 2011 MCI J4500 motor coach equipped with a Detroit Diesel Series 60 engine and a ZF AS Tronic 12-speed automatic transmission. These tests included three load configurations and two different acceleration rates, normal and rapid. Data were gathered with a Racelogic VBOX IIIi.

The time/distance relationship for a motor coach accelerating from a stop is often needed to accurately assess the events leading up to a collision. Several series of tests were conducted to document the low speed acceleration performance of a 45 foot long 2000 Van Hool motor coach equipped with a Cummins ISM engine and an Allison B500 6-speed automatic transmission. These tests included three load configurations and two different acceleration rates, normal and rapid. Data were gathered with a Racelogic VBOX IIIi.

For model year 2018, Freightliner introduced the New Cascadia model to their lineup of Class 8 trucks. Testing of the Freightliner New Cascadia with Detroit Diesel engines was conducted to evaluate the accuracy of the reported event data contained in the engine Electronic Control Units (ECUs) for these trucks. The testing showed that there are occurrences in DDEC Reports, specifically in the Last Stop Record and Hard Braking event data, when the time between successive event data points was two seconds rather than the reported one second interval. The occurrence of the two-second anomaly was not always present in a Last Stop Record or Hard Braking event. When the two-second anomaly was present in the event data, it occurred randomly and no pattern to when this anomaly occurs was determined. No method was found to be able to detect the presence of this anomaly from the review of a Last Stop Record or Hard Braking event.

The time/distance relationship for a heavy truck accelerating from a stop is often needed to accurately assess the events leading up to a collision. Several series of tests were conducted to document the low speed acceleration performance of a 2016 Freightliner Cascadia truck tractor equipped with a 12-speed automated manual transmission in Auto Mode. Unlike tests in previous papers, the driver never manually shifted gears. These tests included three trailer load configurations and two different acceleration rates. Data were gathered with both a VBOX and with the Detroit Diesel Diagnostic Link (DDDL) software.

The time/distance relationship for a heavy truck accelerating from a stop is often needed to accurately assess the events leading up to a collision. Several series of tests were conducted to document the low speed acceleration performance of a 2016 Kenworth T680 truck tractor equipped with a ten-speed overdrive automated manual transmission in Auto Mode. Throughout the testing, the driver never manually shifted gears. This testing included three trailer load configurations and two different acceleration rates. Data were gathered with a VBOX and the Cummins INSITE software.

Computer models used to study crashes require information to describe the vehicles. Information such as weight, length, wheelbase, tire locations, crush stiffness, tire parameters, etc. all require a reliable source. Usually the tire parameters are difficult to obtain. Analysts will routinely use default or “typical” values. In 1999, Engineering Dynamics Corp. (EDC) attempted to address this issue, with support from many in the field of crash reconstruction, by conducting tire tests. The resulting tire test data will be used to study motor vehicle performance. The computer simulations in use today require information about tire properties or lookup tables that must be extracted from raw collected data. This paper presents a basic overview of the tire test data and a technique for extracting the required tire parameters for use in computer simulation modeling.

Rollover collisions sometimes involve a vehicle sliding and plowing on a soft-soil surface. There is little work published on the deceleration rates for a vehicle sliding and plowing in soft soil. Previous tests involving a 4-door sedan sliding sideways and plowing on a soft-soil surface were modeled using the HVE and SIMON 3-dimensional computer simulation program. The plowing forces were modeled using a series of friction multipliers. In addition, an SUV was simulated crossing the same surface in a similar fashion. Results based on these analyzed tests indicate that the average deceleration rate for either vehicle sliding sideways on this soft-soil surface may be approximated by using the vehicle’s static stability factor, or T/2H. This paper presents computer modeling techniques used to analyze overturn crashes. Specifically the SIMON 3-dimensional computer simulation model is used in this work.

Computer simulations are frequently used to analyze occupant kinematics in motor vehicle crashes, including what they collide with during the crash and the severity of these internal collisions. From study of such occupant simulations, it is then possible to infer how the actual human occupants may have been injured in a crash. When using a simulation to study how occupants react in a vehicle crash, a crash-pulse is usually required as input to the occupant simulation model. This crash-pulse is typically generated from a study of the vehicle motion and acceleration during the crash. There are several different methods for obtaining such a crash-pulse which are in common use. Each of these methods produces a different shape for the crash-pulse, even with identical velocity changes for the vehicle. The time duration, maximum acceleration, and general shape of the crash-pulse may influence the predicted motion of the occupants.

The time/distance relationship for a heavy truck starting from a stopped position is often needed to accurately assess the events leading up to a collision. A series of tests were conducted to document the low speed acceleration performance of a Kenworth T2000 tractor- truck equipped with an auto-shift transmission. The tests included several load configurations and acceleration rates. The vehicle was instrumented with a DATRON speed sensor to measure time, distance and speed. This paper presents data from these tests and discusses low speed acceleration profiles of heavy trucks.

The time/distance relationship for a heavy truck starting from a stopped position is often needed to accurately assess the events leading up to a collision. A series of tests were conducted to document the low speed acceleration performance of a Freightliner FLD-120 tractor-truck. The tests including several load configurations and acceleration rates. The vehicle was instrumented with a DATRON speed sensor and the engine RPM was also documented. This paper presents data from these tests and discusses low speed acceleration profiles of heavy trucks