Search Results

The tractor trailer models discussed in this paper were for a real-time hardware-in-the-loop (HIL) simulation to test heavy truck electronic stability control (ESC) systems [1]. The accuracy of the simulation results relies on the fidelity and accuracy of the vehicle parameters used. However in this case where hardware components are part of the simulation, their accuracy also affects the proper working of the simulation and ESC unit. Hence both the software and hardware components have to be validated. The validation process discussed in this paper is divided into two sections. The first section deals with the validation of the TruckSim vehicle model, where experimental data is compared with simulation results from TruckSim. Once the vehicle models are validated, they are incorporated in the HIL simulation and the second section discusses the validation of the whole HIL system with ESC.

Engine control units (ECUs) on heavy trucks have been capable of storing “last stop” or “hard stop” data for some years. These data provide useful information to accident reconstruction personnel. In past studies, these data have been analyzed and compared to higher-resolution on-vehicle data for several heavy trucks and several makes of passenger cars. Previous published studies have been quite helpful in understanding the limitations and/or anomalies associated with these data. This study was designed and executed to add to the technical understanding of heavy truck event data recorders (EDR), specifically data associated with a modern Cummins power plant ECU. Emergency “full-treadle” stops were performed at many combinations of load-speed-surface coefficient conditions. In addition, brake-in-curve tests were performed on wet Jennite for various conditions of disablement of the braking system.

This research paper builds onto the wealth of technical information that has been published in the past by engineers such as Flick, Radlinski, and Heusser. For this paper, the pushrod force versus chamber pressure data published by Heusser are supplemented with data taken from brake chamber types not reported on by Heusser in 1991. The utility of Heusser's braking force relationships is explored and discussed. Finally, a straightforward and robust method for calculating truck braking performance, based on the brake stroke measurements and published heavy truck braking test results, is introduced and compared to full-scale vehicle test data.

This paper is a follow up to a study published in 2005 1 on the same topic and presents a study that was conducted to compare vehicle speed change and acceleration data from full-scale testing to results generated by computer simulation using the SImulation MOdel Non-linear (SIMON) vehicle dynamic simulation model version 3.1 within the Human Vehicle Environment (HVE) software version 5.2. SIMON will be referred to in this paper as the computer or simulation model, while HVE will be referred to as the computer software. In the previous study, a simple method to model the curb was developed and version 2.0 of the simulation model was validated, for delta-v, up to approximately 6.7 m/s (15 mph) and for vertical accelerations, up to speeds of approximately 4.5 m/s (10 mph).

Investigators of vehicular incidents often seek to recover data stored within on-board computer systems. For commercial vehicles, the primary source for this information is the engine control module (ECM). The data stored in these modules, not unlike passenger vehicles, varies widely among manufacturers, as do the hardware and software required to recover such data. Further, the options, and associated risks, involved with attempting to recover this data has a similarly wide variance relative to the engine manufacturer, incident related circumstances, and the tools currently available to perform such downloads. There are two primary paths available to obtain this data: (1) via the vehicle data bus (e.g. SAE J1939 or J1708 ) or (2) direct-to-module (DTM) connection. When using the DTM method, power is applied to an ECM, and the module measures the various engine control and monitoring components for validity.

This paper presents the results of an in-depth study of the measurement of occupant kinematic response on the S-E-A Roll Simulator. This roll simulator was built to provide an accurate and repeatable test procedure for the evaluation of occupant protection and restraint systems during roll events within a variety of occupant compartments. In the present work this roll simulator was utilized for minimum-energy, or threshold type, rollover events of recreational off-highway vehicles (ROVs). Input profiles for these tests were obtained through a separate study involving autonomous full vehicle tests [1]. During simulated roll events anthropomorphic test device (ATD) responses were measured using on-board high speed video, an optical three-dimensional motion capture system (OCMS) and an array of string potentiometers.

According to NHTSA's 2011 Traffic Safety Facts [1], passenger vehicle occupant fatalities continued the strong decline that has been occurring recently. In 2011, there were 21,253 passenger vehicles fatalities compared to 22,273 in 2010, and that was a 4.6% decrease. However; large-truck occupant fatalities increased from 530 in 2010 to 635 in 2011, which is a 20% increase. This was a second consecutive year in which large truck fatalities have increased (9% increase from 2009 to 2010). There was also a 15% increase in large truck occupant injuries from 2010. Moreover, the fatal crashes involving large trucks increased by 1.9%, in contrast to other-vehicle-occupant fatalities that declined by 3.6% from 2010. The 2010 accident statistics NHTSA's report reveals that large trucks have a fatal accident involvement rate of 1.22 vehicles per 100 million vehicle miles traveled compared to 1.53 for light trucks and 1.18 for passenger cars.

This paper describes the design and development of the hardware, electronics, and software components of a state-of-the-art automated steering controller, the SEA, Ltd. ASC. The function of the ASC is to input to a vehicle virtually any steering profile with both high accuracy and repeatability. The ASC is designed to input profiles having steering rates and timing that are in excess of the limits of a human driver. The ASC software allows the user to specify steering profiles and select controller settings, including motor controller gains, through user-interface windows. This makes it possible for the test driver to change steering profiles and settings immediately after running any test maneuver. The motor controller used in the ASC offers self-contained signal input, output, and data storage capabilities. Thus, the ASC can operate as a standalone steering machine or it can be incorporated into typical existing, on-vehicle data acquisition systems.

An Electronic Stability Control algorithm has been developed for All-Terrain Vehicles. The algorithm is implemented on SEA’s Robotic Test Driver which has been customized to drive ATVs unmanned. The ESC algorithm is incorporated in the real-time controller. When activated, ESC monitors vehicle states and when they exceed pre-determined thresholds, ESC intervenes by dropping throttle and applying the vehicle’s brakes. The intention of this algorithm is to prevent yaw instability and ultimately limit the vehicle’s roll angle. ESC is implemented on two vehicles, which exhibit varying degrees of understeer. Test data is provided to illustrate the determination in setting threshold limits. The efficacy of the ESC is demonstrated by showing the system being enabled and disabled.

Brake chamber construction allows for a finite stroke for pushrods during brake application. As such, the Federal Motor Carrier Safety Regulations (FMCSRs) mandate maximum allowable strokes for the various chamber types and sizing. Brake strokes are often measured during compliance inspections and post-accident investigations in order to assess vehicle braking performance and/or capability. A number of studies have been performed, and their results published, regarding the effect of brake stroke and function on braking force and heavy truck stopping performance [1] through [4]. All of the studies have relied on a brake supply pressure of 100 pounds per square inch (psi). When brake strokes are measured in the field, following the Commercial Vehicle Safety Alliance (CVSA) procedure, the application pressure is prescribed to be maintained between 90 and 100 psi.

Evaluating the safety of Autonomous Vehicles (AV) is a challenging problem, especially in traffic conditions involving dynamic interactions. A thorough evaluation of the vehicle’s decisions at all possible critical scenarios is necessary for estimating and validating its safety. However, predicting the response of the vehicle to dynamic traffic conditions can be the first step in the complex problem of understanding vehicle’s behavior. This predicted response of the vehicle can be used in validating vehicle’s safety. In this paper, models based on Machine Learning were explored for predicting and classifying driver’s response. The Naturalistic Driving Study dataset (NDS), which is part of the Strategic Highway Research Program-2 (SHRP2) was used for training and validating these Machine Learning models.

There have been many studies regarding the stability of vehicles following a sudden air loss event in a tire. Previous works have included literature reviews, full-scale vehicle testing, and computer modeling analyses. Some works have validated physics-based computer vehicle simulation models for passenger vehicles and other works have validated models for heavy commercial vehicles. This work describes a study wherein a validated vehicle dynamics computer model has been applied to extrapolate results to higher event speeds that are consistent with travel speeds on contemporary North American highways. This work applies previously validated vehicle dynamics models to study the stability of a five-axle commercial tractor-semitrailer vehicle following a sudden air loss event for a steer axle tire. Further, the work endeavors to understand the analytical tire model for tires that experience a sudden air loss.

Determining impact speeds is an important factor in any accident reconstruction. Event data recorders are now commonplace in on-road vehicles and provide an added tool for the accident reconstructionist. However, in low-speed collisions where impact severity is often important, event data recorders fail to record data as the minimum threshold for impact severity sometimes is not met. Alternatively, damage-based methods may be ineffective in quantifying the severity of the impact due to a lack of defined vehicle crush damage. These types of scenarios oftentimes present themselves as a bullet vehicle in the beginning processes of accelerating from a stop or when a stopped target vehicle is rear-ended from behind by the bullet vehicle.

This paper documents the vehicle response of a tractor-semitrailer following a sudden air loss (Blowout) in a steer axle tire while traveling at highway speeds. The study seeks to compare full-scale test data to predicted response from detailed heavy truck computer vehicle dynamics simulation models. Full-scale testing of a tractor-semitrailer experiencing a sudden failure of a steer axle tire was conducted. Vehicle handling parameters were recorded by on-board computers leading up to and immediately following the sudden air loss. Inertial parameters (roll, yaw, pitch, and accelerations) were measured and recorded for the tractor and semitrailer, along with lateral and longitudinal speeds. Steering wheel angle was also recorded. These data are presented and also compared to the results of computer simulation models. The first simulation model, SImulation MOdel Non-linear (SIMON), is a vehicle dynamic simulation model within the Human Vehicle Environment (HVE) software environment.