Search Results

Accident reconstructionists rarely have complete data with which to determine vehicle speed, and so the true value must be bracketed within a range. Previous work has shown the effect of friction uncertainty in determining speed from tire marks left by a vehicle in yaw. The goal of the current study was to assess improvements in the accuracy of vehicle speed estimated from yaw marks using progressively more scene and vehicle information. Data for this analysis came from staged S-turn maneuvers that in some cases led to rollover of sport utility vehicles. Initial speeds were first calculated using the critical curve speed (CCS) formula on the yaw marks from the first portion of the S-maneuver. Then computer simulations were performed with progressively more input data: i) the complete tire marks from the whole S-maneuver, ii) measured vehicle mass, iii) measured suspension stiffness and damping, and iv) measured steering history.

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.

Vehicle rollovers generate complicated damage patterns as a result of multiple vehicle-to-ground contacts. The goal of this work was to isolate and characterize specific directional features in coarse- and fine-scale scratch damage generated during a rollover crash. Four rollover tests were completed using stock 2001 Chevrolet Trackers. Vehicles were decelerated and launched from a rollover test device to initiate driver's side leading rolls onto concrete and dirt surfaces. Gross vehicle damage and both macroscopic and microscopic features of the scratch damage were documented using standard and macro lenses, a stereomicroscope, and a scanning electron microscope (SEM). The most evident indicators of scratch direction, and thus roll direction, were accumulations of abraded material found at the termination points of scratch-damaged areas. Abrasive wear mechanisms caused local plastic deformation patterns that were evident on painted sheet metal surfaces as well as plastic trim pieces.

Modern vehicles record dynamic data from a number of on-board sensors for events that could precede a crash. These data can be used to reconstruct the behavior of a vehicle, although the accuracy of these reconstructions has not yet been quantified. Here, we evaluated various methods of reconstructing the vehicle kinematics of a 2017 and a 2018 Toyota Corolla based on Vehicle Control History (VCH) data from overlapping events generated by the pre-collision system (PCS), sudden braking (SB) and anti-lock brake (ABS) activation. The vehicles were driven towards a stationary target at 32-64 km/h (20-40 mph) and then after the pre-collision alarm sounded the vehicle was steered sharply right or left and braked rapidly to rest. VCH data for PCS event were recorded at 2 Hz and for the sudden braking and ABS activation events at 6.7 Hz.

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.