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The number of traffic deaths in Japan was 4,612 in 2011. Looking at the road accident fatalities, it revealed that pedestrians accounted for the highest number in 2011 (1,686, 36.6%). To develop safety countermeasures to decrease the severity of injuries and to reduce the number of deaths in traffic accidents, the detailed characteristics of pedestrian injury in vehicle-to-pedestrian crashes are necessary. The purpose of this study is to understand the scenarios of vehicle accidents in which pedestrians suffer fatal injuries. In the present study, we investigated the characteristics of pedestrian injuries in fatal crashes from accident analyses and compared them to head injury severity levels in impact tests against a road pavement and vehicle contact surfaces.

The purpose of this study was to construct driver models using long short-term memory (LSTM) in car-following situations, where other vehicles change lanes and cut in front of the ego vehicle (EGV). The development of autonomous vehicle systems (AVSs) using personalized driver models based on the individual driving characteristics of drivers is expected to reduce their discomfort with vehicle control systems. The driving characteristics of human drivers must be considered in such AVSs. In this study, we experimentally measured data from the EGV and other vehicles using a driving simulator consisting of a six-axis motion device and turntable. The experimental scenario simulated a traffic congestion scenario on a straight section of a highway, where a cut-in vehicle (CIV) changed lanes from an adjacent lane and entered in between the EGV and preceding vehicle (PRV).

Fatal injuries suffered by cyclists in vehicle-versus-cyclist accidents are investigated to provide information for the introduction of safety countermeasures. We analyzed characteristics of cyclist injuries in real fatal accidents and compared them with severity levels of head injury in impact tests against a road surface. In the accident analyses, we investigated the main body regions whose injuries led to fatalities using a macro vehicle-cyclist accident database of the Institute for Traffic Accident Research and Data Analysis of Japan. Using data from 2009 to 2013, we investigated the frequency of cyclist fatalities by gender, age group, vehicle speed, and the source of fatal head injury (impact with the vehicle or road surface). Results indicated that head injuries are the most common cause of cyclist fatalities in car-cyclist accidents.

The present study clarifies the mechanism by which an accident occurs when an elderly pedestrian crosses a road in front of a car, focusing on features of the central and peripheral vision of elderly pedestrians who are judging when it is safe to cross the road. For the pedestrian's central visual field, we investigated the effect of age on the timing judgment using an actual car. The results for daytime conditions indicate that the elderly pedestrians tended to make later judgments of when they crossed the road from the right side of the driver's view at high car velocities. At night, for a car with its headlights on high beam, the average car-pedestrian distances of elderly pedestrians on the left side of the driver's view were significantly longer than those of young pedestrians at velocities of 20 and 40 km/h. The eyesight of the elderly pedestrians during the day did not affect the timing judgment of crossing a road.

This study aims to clarify the relation between axonal deformation and the onset of axonal injury. Firstly, to examine the influence of strain rate on the threshold for axonal injury, cultured neurons were subjected to 12 types of stretching (strains were 0.10, 0.15, and 0.20 and strain rates were 10, 30, 50, and 70 s-1). The formation of axonal swellings and bulbs increased significantly at strain rates of 50 and 30 s-1 with strains of 0.15 and 0.20, respectively, even though those formations did not depend on strain rates in cultures exposed to a strain of 0.10. Then, to examine the influence of the strain along an axon on axonal injury, swellings were measured at every axonal angle in the stretching direction. The axons that were parallel to stretching direction were injured the most. Finally, we proposed an experimental model that subjected an axon to more accurate strain.

The first purpose of this study is to clarify the relation between the car impact velocity and pedestrian injury severity or mortality risk. We investigated the frequency of serious injuries and fatalities of pedestrians using vehicle-pedestrian accident data from the database of the Institute for Traffic Accident Research and Data Analysis (ITARDA) in Japan. The vehicle types considered are sedans, minivans, and box vans (ordinary automobiles) and light passenger cars and light cargo vans (light automobiles). The results revealed that a 10-km/h reduction in impact velocity could mitigate severe pedestrian injuries in cases involving impact velocities of 40 km/h or more for the five vehicle types analyzed. Specifically, if the impact velocity was 30 km/h or less, the frequency of serious injuries was less than 27% and the frequency of fatalities was less than 5% for the five vehicle types.

Vehicles that start moving from a stationary position can cause fatal traffic accidents involving pedestrians. Ultrasonic sensors installed in the vehicle front are an active technology designed to alert drivers to the presence of stationary objects such as rigid walls in front of their vehicles. However, the ability of such sensors to detect humans has not yet been established. Therefore, this study aims to ascertain whether these sensor systems can successfully detect humans. First, we conducted experiments using four vehicles equipped with ultrasonic sensor systems for vehicle-forward moving-off maneuvers and investigated the detection distances between the vehicles and a pipe (1 m long and having a diameter of 75 mm), child, adult female, or adult male. The detections of human volunteers were evaluated under two different conditions: front-facing and side-facing toward the front of each vehicle.

This study aimed to clarify the relationship between truck-pedestrian crash impact velocity and the risks of serious injury and fatality to pedestrians. We used micro and macro truck-pedestrian accident data from the Japanese Institute for Traffic Accident Research and Data Analysis (ITARDA) database. We classified vehicle type into five categories: heavy-duty trucks (gross vehicle weight [GVW] ≥11 × 103 kg [11 tons (t)], medium-duty trucks (5 × 103 kg [5 t] ≤ GVW < 11 × 103 kg [11 t]), light-duty trucks (GVW <5 × 103 kg [5 t]), box vans, and sedans. The fatality risk was ≤5% for light-duty trucks, box vans, and sedans at impact velocities ≤ 30 km/h and for medium-duty trucks at impact velocities ≤20 km/h. The fatality risk was ≤10% for heavy-duty trucks at impact velocities ≤10 km/h. Thus, fatality risk appears strongly associated with vehicle class.

The main purpose of this study is to define the relationship between the car impact velocity and serious injury risk or fatality risk of cyclists. The authors investigated the risks of serious injuries and fatalities of cyclists using vehicle-cyclist accident data from the database of the Institute for Traffic Accident Research and Data Analysis (ITARDA) in Japan. The vehicle types considered are sedans, mini vans, box vans, light passenger cars and light cargo vans. The results revealed that a 10-km/h decrease in the impact velocity could reduce the severe injury risk and fatality risk for impact velocities of 40 km/h or higher. Specifically, when the impact velocity was less than or equal to 30 km/h, the serious injury risks were less than 21% and the fatality risks were less than or equal to 1% for the above listed vehicle types.

This study aimed to clarify the relationship between truck–cyclist collision impact velocity and the serious-injury and fatality risks to cyclists, and to investigate the effects of road type and driving scenario on the frequency of cyclist fatalities due to collisions with vehicles. We used micro and macro truck–cyclist collision data from the Japanese Institute for Traffic Accident Research and Data Analysis (ITARDA) database. We classified vehicle type into five categories: heavy-duty trucks (gross vehicle weight [GVW] ≥11 × 103 kg [11 tons (t)], medium-duty trucks (5 × 103 kg [5 t] ≤ GVW < 11 × 103 kg [11 t]), light-duty trucks (GVW <5 × 103 kg [5 t]), box vans, and sedans. The fatality risk was ≤5% for light-duty trucks, box vans, and sedans at impact velocities ≤40 km/h and for medium-duty trucks at impact velocities ≤30 km/h. The fatality risk was 6% for heavy-duty trucks at impact velocities ≤10 km/h.

In Japan, where vehicles drive on the left side of the road, pedestrian fatal accidents caused by vehicles traveling at speeds of less than or equal to 20 km/h, occur most frequently when a vehicle is turning right. The objective of the present study is to clarify the driving behavior in terms of eye glances and driver speeds when drivers of two different types of vehicles turn right at an intersection on a left-hand traffic road. We experimentally investigated the drivers’ gaze, vehicle speed, and distance on the vehicle traveling trajectory from the vehicle to the pedestrian crossing line, using a sedan and a truck with a gross vehicle weight of < 7.5 tons (a light-duty truck) during right-turn maneuver. We considered four different conditions: no pedestrian dummy (No-P), right pedestrian dummy (R-P), left pedestrian dummy (L-P), and right and left pedestrian dummies (RL-P).

Understanding left-turn vehicle-pedestrian accident mechanisms is critical for developing accident-prevention systems. This study aims to clarify the features of driver behavior focusing on drivers’ gaze, vehicle speed, and time to collision (TTC) during left turns at intersections on left-hand traffic roads. Herein, experiments with a sedan and light-duty truck (< 7.5 tons GVW) are conducted under four conditions: no pedestrian dummy (No-P), near-side pedestrian dummy (Near-P), far-side pedestrian dummy (Far-P) and near-and-far side pedestrian dummies (NF-P). For NF-P, sedans have a significantly shorter gaze time for left-side mirrors compared with light-duty trucks. The light-duty truck’s average speed at the initial line to the intersection (L1) and pedestrian crossing line (L0) is significantly lower than the sedan’s under No-P, Near-P, and NF-P conditions, without any significant difference between any two conditions.