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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.

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.

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.

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.

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.