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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.

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).

For car-following models, the car-following characteristics differ depending on the vehicle type, such as passenger cars, motorcycles, and trucks. Therefore, constructing a model for each category is essential. To that end, various modeling methods have been proposed; however, herein, we particularly focused on the long short-term memory (LSTM), which is the best method for forecasting long-term time-series data.[1, 2] The objective of this study was to construct a car-following model for each vehicle category using the LSTM and to evaluate the model accuracy for each vehicle category. In this study, US-101 and I-80 data provided by the next-generation simulation (NGSIM), which is based on natural traffic flow data, were used. In the NGSIM, only car-following situations were selected as car-following data, and these were classified into the vehicle type: motorcycles, passenger cars, and trucks.

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).

This study aimed to construct driver models for overtaking behavior using long short-term memory (LSTM). During the overtaking maneuver, an ego vehicle changes lanes to the overtaking lane while paying attention to both the preceding vehicle in the travel lane and the following vehicle in the overtaking lane and returns to the travel lane after overtaking the preceding vehicle in the travel lane. This scenario was segregated into four phases in this study: Car-Following, Lane-Change-1, Overtaking, and Lane-Change-2. In the Car-Following phase, the ego vehicle follows the preceding vehicle in the travel lane. Meanwhile, in the Lane-Change-1 phase, the ego vehicle changes from the travel lane to the overtaking lane. Overtaking is the phase in which the ego vehicle in the overtaking lane overtakes the preceding vehicle in the travel lane.

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.

Many automated driving technologies have been developed and are continuing to be implemented for practical use. Among them a driver model is used in automated driving and driver assistance systems to control the longitudinal and lateral directions of the vehicles that reflect the characteristics of individual drivers. To this end, personalized driver models are constructed in this study using long short-term memory (LSTM). The driver models include individual driving characteristics and adapt system control to help minimize discomfort and nuisance to drivers. LSTM is used to construct the driver model, which includes time-series data processing. LSTM models have been used to investigate pedestrian behaviors and develop driver behavior models in previous studies. We measure the driving operation data of the driver using a driving simulator (DS).

This paper examines how vehicle backing technologies affect driver performance during backing maneuvers. We conducted experiments using sport utility vehicles (SUV) with four technological variations: a baseline vehicle (B-L), a vehicle equipped with a parking sensor (PS-V), a vehicle equipped with a backup camera (hereafter BC-V), and a vehicle equipped with both technologies (BCPS-V). Two reverse parking maneuvers were tested: backing straight and backing diagonally into a parking space. For each vehicle, we measured the parameters of the driver’s gaze, vehicle speed, the distance between the stopped vehicle and an object behind it, and the presence or absence of contact with the object. Fifteen drivers participated in the experiment. For backing straight, the B-L and PS-V drivers gazed at the driver-side mirror the longest; BC-V and BCPS-V drivers gazed at the monitor the longest. There was no significant difference in maximum speed among the four backing technology conditions.

Ultrasonic parking sensors are an active technology designed to alert drivers to the presence of objects behind their vehicle but not the presence of a human. The purpose of this study was therefore to ascertain if these sensor systems can successfully detect a human subject. We accordingly conducted experiments using four vehicles equipped with both rear-facing center and corner ultrasonic parking sensor systems to determine the detection distance between the vehicle and a 1-m tall, 75-mm diameter pipe, a child, an adult woman, and an adult man. The detection of human subjects was evaluated under front-facing and side-facing conditions behind each vehicle. The results indicate that for a front-facing and side-facing child, the center sensor detection distances were 50-84% and 32-64%, respectively, shorter than that of the pipe.

SAE International defines six levels of autonomous driving system, four of which include a change of control from the system to the driver in certain conditions. When vehicle control changes from the system to a human driver, a safe transition time is necessary. The present study focuses on level 3 automation, in which the system controls driving in ordinary conditions, but the human driver is expected to intervene in emergency situations. The aim of this study was to investigate the relationship between driver posture and transition time. Driver posture included four components: backrest angle, seat position, foot position, and arm position. These were adjusted to investigate a total of 30 posture patterns. In addition, the situation in which the driver was not watching the road, but instead using a tablet computer was investigated. The driver’s braking and steering reaction times were measured for a highway-driving scenario in which a truck dropped cargo in front of the vehicle.

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.

The purpose of this study is to clarify the mechanism of traffic accidents involving cyclists. The focus is on the characteristics of cyclist accidents and scenarios, because the number of traffic accidents involving cyclists in Tokyo is the highest in Japan. First, dangerous situations in traffic incidents were investigated by collecting data from 304 cyclists in one city in Tokyo using a questionnaire survey. The survey indicated that cyclists used their bicycles generally while commuting to work or school in the morning. Second, the study investigated the characteristics of 250 accident situations involving cyclists that happened in the city using real-world bicycle accident data. The results revealed that the traffic accidents occurred at intersections of local streets, where cyclists collided most often with vehicles during commute time in the morning. Third, cyclists’ behavior was observed at a local street intersection in the morning in the city using video pictures.

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 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.