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This paper describes the instrumentation used to study how the control inputs of 17 long-haul truck drivers were affected by fatigue. The task required outfitting a test vehicle to accurately measure the following control inputs and resulting vehicle behavior: Vehicle speed, Steering wheel angle and angular velocity, Accelerator pedal angle and angular velocity, Perception/response time, Driver EEG and heart rate, Clinical assessment of driver fatigue, Vehicle lateral lane position, and Car-following distance. The location and mounting procedure of each instrument as well as the sampling requirements for each device are discussed. Also discussed are the methods of data handling and storage.

The driving performance of 17 heavy-truck drivers was monitored under alert and fatigued conditions on a closed-circuit track to determine whether driver fatigue could be indirectly measured in the vehicle control inputs or outputs. Data were recorded for various potential physiological indicators of fatigue (EEG, heart rate and a subjective evaluation of drowsiness), for vehicle speed, steering, and accelerator pedal movements, and for vehicle position on the track. The objective was to determine whether a simple set of vehicle-based control measures correlated with the fatigue indicators. Correlations between other vehicle-based measures reported in the literature and the fatigue indicators were also calculated. The results indicate that there are measures which correlate sufficiently well with driver fatigue that they could potentially be used for an unobtrusive vehicle-based fatigue-detection algorithm.

This paper continues the analysis of data published previously, focusing on steering wheel behavior and its correlation with driver fatigue (as measured by EEG, heart rate, and subjective evaluation of drowsiness from video). New steering-based weighting functions devised from observed changes in steering wheel motions are presented. Significant correlations between the weighting functions and the measures of driver fatigue suggest that some of the functions could form the basis of a fatigue-detection algorithm.

In order to assess the head and neck kinetics of human subjects exposed to low-speed rear-end impacts, a method for measuring the magnitude and line of action of the force between the head and the head restraint was required. In addition to being accurate and repeatable, the design was required to maintain original seat back and head restraint geometry, mass, stiffness, and height adjustment. This paper presents a design using strain gauges applied to the head restraint tubes, upper seat back, and custom replacements for brackets attaching the head restraint to the seat back. The background theory and free-body analysis, the analog math circuitry, and a dynamic calibration procedure are presented. Overall force magnitude and line-of-action errors are quantified, and a sample output from a human subject undergoing a rear-end collision with a speed change of 8 km/h is presented.

There are many bumper-to-bumper automobile collisions in which a vehicle occupant claims injury but where there is little or no outward damage to the vehicles. On vehicles equipped with shock-absorber-type bumper isolators, the only “damage” often consists of compression marks left on the isolator piston tube and scuffs on the bumper. This paper examines the behavior of specific automobile bumpers in aligned low-velocity collisions. Specifically, empirical data gathered during numerous (currently 660) vehicle-to-vehicle and vehicle-to-barrier collisions are presented and relationships between isolator compression and vehicle impact severity are developed. General trends among all types of isolators and trends specific to vehicle manufacturers are identified and discussed. Damage threshold data are also presented.

This paper evaluates the accuracy of different methods for determining the speed change during vehicle-to-vehicle collisions from isolator compression and low-speed barrier data. A controlled regimen of 938 aligned, low-speed collisions was completed, including a series in which collision force data were collected to compare vehicle-to-barrier and vehicle-to-Vehicle Collisions. Five vehicles (four with isolators and one with a foam-core bumper) were tested against a rigid barrier and against each other in collisions below damage threshold. Three methods of assessing the speed change of a low-speed vehicle-to-vehicle collision are evaluated as alternatives to a fourth method: staging collisions with exemplar vehicles. For each of the three methods, the expected accuracy and limitations are presented.

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.

Recent epidemiological evidence shows that the potential for whiplash injury varies with both the average acceleration and speed change of a rear-end collision. The goal of this study was to examine the gradation of neck muscle responses and the head and neck kinematics to rear-end collision pulses in which the acceleration and speed change were independently varied. Thirty subjects (15F, 15M) underwent 36 consecutive rear-end collisions consisting of three different average accelerations (ā = 0.5, 0.9 and 1.3 g) and three different speed changes (Δv = 0.25, 0.50 and 0.75 m/s). Onset and amplitude of the sternocleidomastoid (SCM) and cervical paraspinal (PARA) muscle responses were measured using surface electromyography. Kinematic measures included linear and angular accelerations and displacements of the head and torso. The results showed that the amplitude of the muscle and kinematics responses was graded to both collision acceleration and speed change.

The repeatability and accuracy of front and rear speed changes reported by Toyota’s Airbag Control Modules (ACMs) have been previously characterized for low-severity collisions simulated on a linear sled. The goals of the present study are (i) to determine the accuracy and repeatability of Toyota ACMs in mid-severity crashes, and (ii) to validate the assumption that ACMs function similarly for idealized sled pulses and full-scale vehicle-to-barrier and vehicle-to-vehicle crashes. We exposed three Toyota Corollas to a series of full-scale aligned frontal and rear-end crash tests with speed changes (ΔV) of 4 to 12 km/h. We then characterized the response of another 16 isolated Toyota ACMs from three vehicle models (Corolla, Prius and Camry) and 3 generations (Gen 1, 2 and 3) using idealized sled pulses and replicated vehicle-to-vehicle and vehicle-to-barrier pulses in both frontal and rear-end crashes (ΔV = 9 to 17 km/h).

Newer Toyota vehicles store information about more than 50 parameters for 5 s before and after non-collision events in the Vehicle Control History (VCH) records. The goals of this study were to assess the accuracy of VCH data acquired during Autonomous Emergency Braking (AEB) events and to investigate the effects of speed, acceleration, and system settings on AEB performance. A 2017 Toyota Corolla with Safety Sense P Pre-Collision System (PCS) was driven in a straight line towards a car-like target at different combinations of four speeds (20, 25, 30, and 40 km/h; or 12, 15, 19, and 25 mph) and three accelerator pedal positions (constant 30%, 40%, and 50% accelerator opening ratios) until the AEB system activated. The vehicle speed, vehicle acceleration, radar target closing speed, and radar target distance recorded in the VCH were compared to a reference 5th wheel. We found that errors in the VCH distance, speed, and acceleration data varied with the test conditions.

The Pre-Collision System (PCS) in Toyota’s Safety Sense package includes an autonomous emergency braking feature that can stop or slow a vehicle independent of driver input if there is an impending collision. The goals of this study were to determine how hazard characteristics, specifically radar reflector size and degree of target edge contrast, affect the response of the PCS, as well as to scrutinize tests wherein the PCS failed to stop the vehicle before impact. We conducted 80 tests with a 2017 Toyota Corolla driven towards a car-like target in a straight line and under constant accelerator pedal position, reaching about 30 km/h at the PCS alarm. Vehicle speed and distance to target at the alarm flag (ALM) and at times corresponding to three other system flags (PBA, FPB, and PB) were read from the Vehicle Control History records. Time to impact (TTI) at each flag was calculated and the distance between the stopped vehicle and the target was measured for each test.

Limited data exist which quantify the kinematic response of the human head and cervical spine in low-speed rear-end automobile collisions. The objectives of this study were to quantify human head/neck kinematics and how they vary with vehicle speed change and gender during low-speed rear-end collisions. Forty-two human subjects (21 male, 21 female) were exposed to two rear-end vehicle-to-vehicle impacts (speed changes of 4 kmlh and 8 km/h). Accelerations and displacements of the head and torso were measured using 6 degree-of-freedom accelerometry and sagittal high speed video respectively. Velocity was calculated by integrating the accelerometer data. Kinematic data of the head and C7-T1 joint axis in the global reference frame, and head kinematic data relative to the C7-T1 joint axis are presented. A statistical comparison between peak amplitude and time-to-peak amplitude for thirty-one common peaks in the kinematic response was performed.

An anthropomorphic test device (ATD) which accurately models the kinematic and kinetic responses of human subjects during head restraint contact in low-speed rear-end collisions is needed to evaluate present and future seat and vehicle designs. The primary goal of this study was to quantify the biofidelity of a new rear-impact ATD, the RID2a, by comparing its dynamic response to those of human subjects under identical test conditions. For this study, a RID2a and a Hybrid III ATD were each exposed to 10 low-speed rear-end collisions: five at a speed change of 4 km/h and five at a speed change of 8 km/h. Sagittal plane kinematics of the head and upper torso, head restraint contact forces, and the reaction loads and moment at the atlanto-occipital joint were determined and compared to the response of eleven male human subjects. Both ATDs produced repeatable response corridors. As observed by others, the Hybrid III did not replicate many features of the human response.

Adrian's visibility model is a useful tool for assessing the visibility of an object at night. However, it was developed under laboratory conditions. Thus, it is necessary to determine the visibility levels which are required for detection under nighttime driving conditions. Experimental data from Olson et al were applied to the Adrian visibility model to determine visibility levels at target detection for alerted drivers. The data has been modified to account for experimental delay in the recorded detection points and a correction has been applied to assess driver expectation. Driver age, headlight beam pattern, and target reflectivity were all found to have a significant effect on visibility level at target detection. For alerted drivers, 50th-percentile threshold visibility levels between 1 and 23 were calculated. For unalerted drivers, 50th-percentile threshold visibility levels between 13 and 210 were calculated.

The goal of this study was to use naturalistic driving data to characterize the longitudinal and lateral accelerations of vehicles making a left turn from a stop at signalized intersections. Left turn maneuvers at 15 intersections were extracted from the Second Strategic Highway Research Program (SHRP2) database. A subset of 420 traversals for lead vehicles that were initially stopped and negotiated their left turns unimpeded by oncoming traffic was used for the analysis. For each traversal, we extracted information regarding the driver’s sex and age, the vehicle type, the vehicle’s longitudinal and lateral acceleration, and on-board forward-facing video. From the video, we further extracted information about whether the road was dry/wet and if it was day/night, and from aerial photographs of the intersections we extracted the radius of each left turn path through the intersection.

Bicycle computers record and store global position data that can be useful for forensic investigations. The goal of this study was to estimate the absolute error of the latitude and longitude positions recorded by a common bicycle computer over a wide range of riding conditions. We installed three Garmin Edge 530 computers on the handlebars of a bicycle and acquired 9 hours of static data and 96 hours (2214 km) of dynamic data using three different navigation modes (GPS, GPS+GLONASS, and GPS+Galileo satellite systems) and two geographic locations (Vancouver, BC, Canada and Orange County, CA, USA). We used the principle of error propagation to calculate the absolute error of this device from the relative errors between the three pairs of computers. During the static tests, we found 16 m to 108 m of drift during the first 4 min and 1.4 m to 5.0 m of drift during a subsequent 8 min period. During the dynamic tests, we found a 95th percentile absolute error for this device of ±8.04 m.

The goal of this study was to use naturalistic driving data to characterize the motion of vehicles making right turns at signalized intersections. Right-turn maneuvers from 13 intersections were extracted from the Second Strategic Highway Research Program (SHRP2) database and categorized based on whether or not the vehicle came to a stop prior to making its turn. Out of the vehicles that did stop, those that were the first and second in line at the intersection were isolated. This resulted in 186 stopped first-in-line turns, 91 stopped second-in-line turns, and 353 no stop turns. Independent variables regarding the maneuver, including driver’s sex and age, vehicle type, speed, and longitudinal and lateral acceleration were extracted. The on-board video was reviewed to categorize the road as dry/wet and if it was day/night. Aerial photographs of the intersections were obtained, and the inner radius of the curve was measured using the curb as a reference.