Search Results

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.

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.

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.

There are limited scientific data available on the acceleration and braking performance of modern snowmobiles. In this study we investigated the acceleration and deceleration characteristics of four modern snowmobiles of varying engine size (500 to 1000 cc) and style (2-stroke and 4-stroke) on groomed/packed snow conditions. The acceleration tests were performed at quarter, half and full throttle. The deceleration tests were performed using full braking with locked tracks and rolldown with power both on and off. Target test speeds ranged from 20 to 60 km/h. Snow condition parameters were measured throughout the tests. The results of the acceleration tests showed that at higher speeds, higher horsepower rating generally corresponds to higher acceleration rates, with a maximum observed average acceleration of 0.70g.

In low-speed collisions, motor vehicles can lose a significant fraction of their initial kinetic energy without plastic deformation or damping elements in their bumper assemblies. Five vehicles were subjected to multiple, non-damaging barrier and vehicle-to-vehicle impacts. Position, velocity, acceleration and force data were recorded for all collisions. Modeling vehicles as non-rigid two degree of freedom systems accurately predicted velocity and restitution responses for five vehicles in barrier and vehicle-to-vehicle impacts.

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.

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.

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.

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.

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 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.