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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.

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.

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).

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.

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.

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.

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 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.