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In 2006, there were approximately 23,500 rear-end crashes involving heavy trucks (i.e., gross vehicle weight greater than 4,536 kg). The Enhanced Rear Signaling (ERS) for Heavy Trucks project was developed by the Federal Motor Carrier Safety Administration (FMCSA) to investigate methods to reduce or mitigate those crashes where a heavy truck has been struck from behind by another vehicle. Visual warnings have been shown to be effective, assuming the following driver is looking directly at the warning display or has his/her eyes drawn to it. A visual warning can be placed where it is needed and it can be designed so that its meaning is nearly unambiguous. FMCSA contracted with the Virginia Tech Transportation Institute (VTTI) to investigate potential benefit of additional rear warning-light configurations as rear-end crash countermeasures for heavy trucks.

Safety and minimal transit time are vital during transportation of essential commodities and passengers, especially in winter conditions. Icy roads are the worst driving conditions with the least available friction, leaving valuable cargo and precious human lives at stake. The study investigates the available friction at the tire-ice interface due to changes in key operational parameters. Experimental analysis of tractive performance of tires on ice was carried out indoor, using the terramechanics rig located at the Advanced Vehicle Dynamics Laboratory (AVDL) at Virginia Tech. The friction-slip ratio curves obtained from indoor testing were inputted into TruckSIM, defining tire behavior for various ice scenarios and then simulating performance of trucks on ice. The shortcomings of simulations in considering the effects of all the operational parameters result in differences between findings of indoor testing and truck performance simulations.

Vehicle analytical models are often favorable due to describing the physical phenomena associated with vehicle operation following from the principles of physics, with explainable mathematical trends and with extendable modeling to other types of vehicle. However, no experimentally validated analytical model has been developed as yet of diesel engine fuel consumption rate. The present paper demonstrates and validates for trucks and light commercial vehicles an analytical model of supercharged diesel engine fuel consumption rate. The study points out with 99.6% coefficient of determination that the average percentage of deviation of the steady speed-based simulated results from the corresponding field data is 3.7% for all Freeway cycles. The paper also shows with 98% coefficient of determination that the average percentage of deviation of the acceleration-based simulated results from the corresponding field data under negative acceleration is 0.12 %.

2011 - 56th L. Ray Buckendale Lecture Driver distraction has become an important topic in society and the research community. A telltale sign of how driver distraction has impacted society is evidenced by the designation of the term "distracted driving" as Webster's New World® College Dictionary 2009 Word of the Year. Since the release of a key study directed at commercial vehicle drivers, there have been two U.S. Department of Transportation summits to address the topic, in addition to legislation banning texting-while-driving in commercial motor vehicles. Given that "driver distraction" is a construct without a consensus definition, many studies on driver distraction have focused on its fundamental and theoretical underpinning, which is a critical first step in understanding the phenomenon.

To help drivers monitor the road and to reduce blind spots, Camera/Video Imaging Systems (C/VISs) display live video from cameras mounted on the truck's exterior to drivers using displays inside the truck cabin. This study investigated drivers' performance with C/VISs in a real-world trucking operation. Twelve commercial drivers' performance with and without a C/VIS was continuously recorded while they each drove for four months. Half of the drivers used a commercially available C/VIS that had a side-view camera on each fender. The other drivers used an advanced C/VIS (A-C/VIS) that had side-view cameras, a rear-view camera, and night-vision capabilities. This paper presents the study's final results and expands on the preliminary results that were previously reported. Detailed analyses of drivers' involvement in Safety-Critical Events (SCEs), their lane change performance, and their opinions of the C/VISs are presented.

Approximately 28,000 crashes involving combination unit trucks occur each year when they are making lane changes, merges, or turns. One contributing factor in these crashes is inadequate visibility for truck drivers. Recent advances in video technology have heightened the prospect of improving commercial vehicle safety by improving drivers' vision around the truck. For such video systems to be implemented on heavy trucks, the systems/driver interface should be demonstrated as viable through research. This paper presents the Camera/Video Imaging Systems (C/VISs) developed at Virginia Tech Transportation Institute (VTTI), the methodology used to test them, and some results obtained.

Camera/Video Imaging Systems (C/VISs) display video captured from cameras mounted on the truck's fenders and trailer to drivers using displays mounted inside the truck cabin. C/VISs provide a countermeasure to blind-spot related crashes by allowing drivers to see objects not ordinarily visible by a typical mirror configuration. They also support drivers in determining the clearance between the trailer and an adjacent vehicle when performing a lane change. The National Highway Traffic Safety Administration (NHTSA) and the Federal Motor Carrier Safety Association (FMCSA) have collaboratively funded research on the development of C/VISs that operate during the day, as well as enhancing C/VISs to operate at night and in inclement weather. This paper presents the work performed in developing a C/VIS capable of being used in an eight-month technology field demonstration (TFD), which will allow the measurement of driver behavior with the C/VIS in a revenue-producing environment.

On-board Camera/Video Imaging Systems (C/VISs) for heavy vehicles display live images to the driver of selected areas to the sides, and in back of the truck's exterior using displays inside the truck cabin. They provide a countermeasure to blind-spot related crashes by allowing drivers to see objects not ordinarily visible by a typical mirror configuration, and to better judge the clearance between the trailer and an adjacent vehicle when changing lanes. The Virginia Tech Transportation Institute is currently investigating commercial motor vehicle (CMV) driver performance with C/VISs through a technology field demonstration sponsored by the National Highway Traffic Safety Administration (NHTSA) and the Federal Motor Carrier Safety Administration (FMCSA). Data collection, which consists of recording twelve CMV drivers performing their daily employment duties with and without a C/VIS for four months, is currently underway.

This paper studies the effect of different longitudinal load conditions, roundabout cross-sectional geometry, and different semi-truck pneumatic suspension systems on roll stability in roundabouts, which have become more and more popular in urban settings. Roundabouts are commonly designed in their size and form to accommodate articulated heavy vehicles (AHVs) by evaluating such affects as off-tracking. However, the effect of the roadway geometry in roundabouts on the roll dynamics of semi-tractors and trailers are equally important, along with their entry and exit configuration. , Because the effect of the roundabout on the dynamics of trucks is further removed from the immediate issues considered by roadway planner, at times they are not given as much consideration as other roadway design factors.

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.

Heavy-vehicle event data recorders (HVEDRs) provide a source of temporal vehicle data just prior to, during, and for a short period after, an event. In the 1990s, heavy-vehicle (HV) engine manufacturers expanded the capabilities of engine control units (ECU) and engine control modules (ECM) to include the ability to record and store small amounts of parametric vehicle data. This advanced capability has had a significant impact on vehicle safety by helping law enforcement, engineers, and researchers reconstruct events of a vehicle crash and understand the details surrounding that vehicle crash. Today, EDR technologies have been incorporated into a wide range of heavy vehicle (HV) safety systems (e.g., crash mitigation systems, air bag control systems, and behavioral monitoring systems). However, the adoption of EDR technologies has not been uniform across all classes of HVs or their associated vehicle systems.

Low-speed automated vehicles (LSAVs) are being deployed in various scenarios to enhance mobility for a wide variety of transportation users. LSAVs are typically highly automated battery-electric vehicles that transport up to eight passengers at speeds below 15 mph on predefined and previously mapped routes. Current applications include providing last-mile connectivity and serving as circulating shuttles in areas such as business districts, military bases, parking lots, and theme parks. An EasyMile EZ10 LSAV was deployed on a route between the Virginia Tech Transportation Institute (VTTI) campus and a nearby bus transit stop as part of a study focusing on prospective user attitudes and acceptance with regard to trust in technology, system safety, and personal security. The LSAV operated on this route within normal travel lanes and interacted with mixed public traffic that included the full range of transportation users from pedestrians to heavy vehicles.

Structural Health Monitoring (SHM), especially in the field of rotary machinery diagnosis, plays a crucial role in determining the defect category as well as its intensity in a machine element. This paper proposes a new framework for real-time classification of structural defects in a roller bearing test rig using time domain-based classification algorithms. Along with the bearing defects, the effect of eccentric shaft loading has also been analyzed. The entire system comprises of three modules: sensor module – using accelerometers for data collection, data processing module – using time-domain based signal processing algorithms for feature extraction, and classification module – comprising of deep learning algorithms for classifying between different structural defects occurring within the inner and outer race of the bearing.

This paper provides a simulation analysis of a novel interconnected roll stability control (RSC) system for improving the roll stability of semitrucks with double trailers. Different from conventional RSC systems where each trailer’s RSC module operates independently, the studied interconnected RSC system allows the two trailers’ RSC systems to communicate with each other. As such, if one trailer’s RSC activates, the other one is also activated to assist in further scrubbing speed or intervening sooner. Simulations are performed using a multi-body vehicle dynamics model that is developed in TruckSim® and coupled with the RSC model established in Simulink®. The dynamic model is validated using track test data. The simulation results for a ramp steer maneuver (RSM) and sine-with-dwell (SWD) maneuver indicate that the proposed RSC system reduces lateral acceleration and rollover index for both trailers, decreasing the likelihood of wheel tip-up and vehicle rollover.

One of the principal goals of modern vehicle control systems is to ensure passenger safety during dangerous maneuvers. Their effectiveness relies on providing appropriate parameter inputs. Tire-road contact forces are among the most important because they provide helpful information that could be used to mitigate vehicle instabilities. Unfortunately, measuring these forces requires expensive instrumentation and is not suitable for commercial vehicles. Thus, accurately estimating them is a crucial task. In this work, two estimation approaches are compared, an observer method and a neural network learning technique. Both predict the lateral and longitudinal tire-road contact forces. The observer approach takes into account system nonlinearities and estimates the stochastic states by using an extended Kalman filter technique to perform data fusion based on the popular bicycle model.

This paper presents the results of a study on the effect of truck configurations on the roll stability of commercial trucks in roundabouts that are commonly used in urban settings with increasing frequency. The special geometric layout of roundabouts can increase the risk of rollover in high-CG vehicles, even at low speeds. Relatively few in-depth studies have been conducted on rollover stability of commercial trucks in roundabouts. This study uses a commercially available software, TruckSim®, to perform simulations on four truck configurations, including a single-unit truck, a WB-67 semi-truck, the combination of a tractor with double 28-ft trailers, and the combination of a tractor with double 40-ft trailers. A single-lane and multilane roundabout are modeled, both with a truck apron. Three travel movements through the roundabouts are considered, including right turn, through-movement, and left turn.

This study evaluated the performance of a new approach for detecting problems with commercial vehicle brakes based on the analysis of sounds emitted during braking. Commercial vehicle brakes emit ultrasonic energy inaudible to humans as part of the friction process, and the spectral distribution of these sounds is highly dependent on the mechanical condition of the brakes. Data collected from a commercial vehicle fleet found that the acoustic signature changes as friction linings wear. This conforms with the acoustic theory that the resonant frequency of an object increases with its decrease in mass. The use of this information to inform maintenance operations is promising in that the scheduling of visual brake inspections could be based on acoustic wear patterns rather than arbitrary time intervals and the observation of anomalous signals that might indicate more immediate concerns.

This study evaluated the performance of heavy vehicle crash avoidance systems (CASs) by collecting naturalistic driving data from 150 truck tractors equipped with Meritor WABCO OnGuardTM or Bendix® Wingman® AdvancedTM products. These CASs provide drivers with audio-visual alerts of potential conflicts, and can apply automatic braking to mitigate or prevent a potential collision. Each truck tractor participated for up to one year between 2013 and 2015. Videos of the forward roadway and drivers’ faces were collected along with vehicle network data while drivers performed their normal duties on revenue-producing routes. The study evaluated the performance of CAS activations by classifying them into three categories based on whether a valid object was being tracked and whether drivers needed to react immediately.

Large trucks were involved in more than 26,000 crashes between April 2001 and December 2003 as a result of making lane changes, merges, and turns [1]. As an alternative to mirrors (surrogate system), or to be used in combination with mirrors (enhancement system), the industry has been developing Camera/Video Imaging Systems (C/VISs) directed toward improving visibility to the sides and rear of heavy vehicles. The current study describes development of an Enhanced C/VIS (E-C/VIS) directed at improving visibility in less favorable environmental conditions, such as nighttime and inclement weather.

Vehicle headlamps and roadway lighting are the major sources of illumination at night. These sources affect contrast - defined as the luminance difference of an object from its background - which drives visibility at night. However, the combined effect of vehicle headlamps and intersection lighting on object contrast has not been reported previously. In this study, the interactive effects of vehicle headlamps and overhead lighting on object contrast were explored based on earlier work that examined drivers’ visibility under three intersection lighting designs (illuminated approach, illuminated box, and illuminated approach + box). The goals of this study were to: 1) quantify object luminance and contrast as a function of a vehicle’s headlamps and its distance to an intersection using the three lighting designs; and, 2) to assess whether contrast influences visual performance and perceived visibility in a highly dynamic intersection environment.