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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.

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).

Most driving assist systems are uniformly controlled without considering differences in characteristics of individual drivers. Drivers may feel discomfort, nuisance, and stress if the system functions differently from their characteristics. The present study reduced these side effects for systems with a highly accurate driver model. The model was constructed using Nonlinear AutoregRessive with eXogeous inputs (NARX), which has a learning function and estimates the driving action of a driver. The model was constructed for one driving condition yet can be applied to other driving conditions. If one model can be applied to many driving conditions, a system can construct as minimum requirements. The driver decelerated while approaching the target at the tail of a traffic jam on a highway. A driver model was constructed for the driver’s braking action. The experimental condition was 11 data measurements from 50 to 130 km/h made at intervals of 10 km/h.

Two experiments were carried out to clarify the characteristics of manual driving when the task of vehicle control is transferred from an autonomous driving system at SAE levels 3 and 5 to manual driving. The first experiment involved another vehicle merging into the lane of the host vehicle from the left side of a highway. This experiment simulated the functional limit of a level 3 system with the driver in a situation of low alertness. When the other vehicle changed lane in front of the host vehicle, the driving task was transferred from the system to the driver. The second experiment simulated a driver travelling along a city road with manual driving after the driver used the system in a situation of sleeping on a highway. In this experiment, a pedestrian emerges from a blind spot along a city road, and the driver needs to brake having recently awaken. In the first experiment, the driver with low alertness could not control the vehicle when manually driving.

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.

A vehicle-to-vehicle communication system (V2V) sends and receives vehicle information by wireless communication and assists safe driving. The present study investigated the activation timings of collision information support, collision caution support, and collision warning support provided by a V2V in an experiment using a driving simulator for four situations of (1) assistance in braking, (2) assistance in accelerating, (3) assistance in making a right turn, and (4) assistance in making a left turn at a blind intersection. The four situations are common scenarios of traffic accidents in Japan. Safety margins for collision information support and collision warning support were the time required for the driver to fully apply the brake pedal, while the safety margin for collision caution support was the time required for the driver to begin applying the brake pedal. The study investigated the effects of adding safety margins to standard activation timings.

Vehicle to vehicle communication system (V2V) can send and receive the vehicle information by wireless communication, and can use as a safety driving assist for driver. Currently, it is investigated to clarify an appropriate activation timing for collision information, caution and warning in Japan. This study focused on the activation timing of collision information (Provide objective information for safe driving to the driver) on V2V, and investigated an effective activation timing of collision information, and the relationship between the activation timing and the accuracy of the vehicle position. This experiment used Driving Simulator. The experimental scenario is four situations of (1) “Assistance for braking”, (2) “Assistance for accelerating”, (3) “Assistance for right turn” and (4) “Assistance for left turn” in blind intersection. The activation timing of collision information based on TTI (Time To Intersection) and TTC (Time To Collision).

This study investigated the driving characteristics of drivers when the system changes from autonomous driving to manual driving in the case of low driver alertness. The analysis clarified the difference in driving characteristics between cases of normal and low driver alertness. In the experiments, driver's alertness states varied from completely alert (level 1) to asleep (level 5). The experimental scenario was that the host vehicle drives along a highway at 27.8 m/s (100km/h) under the control of the autonomous system. The operation of the autonomous system is suspended, and the mode of autonomous driving changes to a mode of manual driving as the other vehicle pulls in front of the host vehicle. The driver then avoids a collision with the other vehicle with him/herself in control. The alertness level of drivers was determined from a previously developed method of examining video of the driver's face and their actions.

Although traffic accidents in Japan involving bicycles have been decreasing yearly, more than 120,000 per year still occur. Few data exist regarding the mechanisms underlying bicycle accidents occurring at intersections. Such dangerous situations form the backdrop of the warning and automatic braking systems being developed for motor vehicles. By clarifying cyclist behavioral characteristics at crucial times, it may be possible to introduce a similar warning system for cyclists as a countermeasure to reduce accidents. The objective of this study is to clarify the mechanism of accidents involving bicycles and to obtain useful data for the development of a warning system for cyclists. A video camera and software investigated and analyzed cyclists' speed and trajectory at an intersection where many accidents occur. Cyclists entering the intersection from one direction were recorded.

Collision avoidance systems for rear-end collisions have been researched and developed. It is necessary to activate collision warnings and automatic braking systems with appropriate timing determined by a monitoring system of a driver's braking action. Although there are various systems to monitor driving behavior, this study aims to create a monitoring system using a driver model. This study was intended to construct a model of a driver's braking action with the Time Delay Neural Network (TDNN). An experimental scenario focuses on rear-end collisions on a highway, such as the driver of a host vehicle controlling the brake to avoid a collision into a leading vehicle in a stationary condition caused by a traffic jam. In order to examine the accuracy of the TDNN model, this study used four parameters: the number of learning, the number of neurons in the hidden layer, the sampling time with 0.01 second as a minimum value, and the number of the delay time.

At the time of a vehicle fire, high pressure hydrogen gas in a tank (a high pressure hydrogen gas cylinder) of a fuel cell vehicle (FCV), which is a passenger vehicle, must exhaust through a pressure relief device (PRD) as quickly as possible in order to prevent any accidental bursts by a temperature rise of hydrogen gas in the cylinder. The high temperature region surrounding a vehicle develops when the hydrogen gas is released through a small nozzle to the air directly. Therefore, to suppress the high temperature region, the effectiveness of a diffusion box is considered further. A pressure relief device (PRD) detects differences in temperature of the environment surrounding an FCV on fire and releases hydrogen gas in a tank to the air by which the valve opens when the temperature in the environment becomes high. The PRD also releases hydrogen gas through a nozzle, e.g. installed upward or downward, to the outside of the vehicle. The PRD is required to be installed in an FCV.

BAS assists driver's by automatically increasing their braking power during an emergency brake event when the driver is unable to apply a sufficient brake force.. There are two performance requirements that BAS must fulfill in order to be employed effectively. One is the ability to activate when the driver suddenly applies brakes in an emergency while the other is the ability to provide additional assistance. Further study of BAS activation timing and degree of assistance in relation to driver acceptance is needed. The driver's acceptance of BAS refers to the BAS activation only during an emergency. A study was conducted to clarify drivers' emergency braking characteristics and measure the frequency of BAS activation during normal braking. One aim of the study was to verify driver characteristics during emergency braking on a test course.

Fuel cell vehicles that use high pressure hydrogen gas as a fuel should be able to immediately release hydrogen gas from the cylinder through pressure relief devices (PRDs) in the event of a vehicle fire. The release through PRDs prevents the cylinder from exploding due to the increased pressure of hydrogen gas, but the method of releasing the gas needs to be specified in order to avoid secondary disaster due to the spread of fire. Since hydrogen cylinders for fuel cell buses are different in terms of installation location and size from those for ordinary vehicles, the location of PRDs and the release direction of hydrogen gas should be separately examined. For example, the improper locations of PRDs would raise the possibility of explosion because of a delay in temperature rise, and the direct release of hydrogen gas from a cylinder installed on the rooftop of the bus may disperse the flame over a wide area.