Toshihisa Sato

Predicting Driver Behavior Using Field Experiment Data and Driving Simulator Experiment Data: Assessing Impact of Elimination of Stop Regulation at Railway Crossings

We investigated the impact of deregulating the presence of stop signs at railway crossings on car driver behavior. We estimated the probability that a driver would stop inside the crossing, thereby obstructing the tracks, when a lead vehicle suddenly stopped after the crossing and a stop regulation was eliminated. We proposed a new assessment method of the driving behavior as follows: first, collecting driving behavior data in a driving simulator and in a real road environment; then, predicting the probability based on the collected data. In the simulator experiment, we measured the distances between a lead vehicle and the driver’s vehicle and the driver’s response time to the deceleration of the leading vehicle when entering the railway crossing. We investigated the influence of the presence of two leading vehicles on the driver’s vehicle movements. The deceleration data were recorded in the field experiments. Slower driving speed led to a higher probability of stopping inside the railway crossing. The probability was higher when the vehicle in front of the leading vehicle did not slow down than when both the lead vehicle and the vehicle in front of it slowed down. Finally, advantages of our new assessment method were discussed.

Preliminary study on driver acceptance of multiple warnings while driving on highway

We conducted driving simulator experiments with 65 participants and investigated drivers acceptance of multiple warnings (visual and auditory displays) including forward collision warning system and lateral collision warning system while driving on Metropolitan Expressway. Three kinds of warning presentation timing (normal, long, and very long) and two kinds of sound sources of the auditory message (with and without separate placed speakers) were used in the experimental trials. We evaluated drivers acceptance of the multiple warnings by means of subjective assessment techniques as well as the behavior assessment. The results of subjective and objective measurement suggest that drivers prefer to longer warning presentation timing for accurate understanding of the provided warnings and the separate placed speakers contribute to an easy detection of the lateral collision warning via the provided sound.

Understanding Driver Car-Following Behavior Using a Fuzzy Logic Car-Following Model

Recently, automatic systems that control driving speeds and headway distances while following a vehicle have been developed worldwide. Some products, such as adaptive cruise control systems, have already been installed in upper segments of passenger vehicles. Car following is an important operation in safe and comfortable driving on straight and/or curved roads. The number of traffic accidents involving rear-end collisions is the highest over the last decade in Japan (Iwashita et al., 2011). A rear-end collision occurs when the distance between two vehicles decreases due to deceleration of the lead vehicle and/or higher speed of the following vehicle. The automatic vehicle control system maintains a safe headway distance while following a vehicle and controls velocity according to the relative speed of the leading vehicle, in order to avoid a rear-end collision.

Analysis of naturalistic driving behavior while approaching an intersection and implications for route guidance presentation

This study focuses on an analysis of the naturalistic driving behavior before making a right turn at an intersection. We conducted experiments on a public road and measured driver behavior, vehicle state, and headway and rear distances. The results suggest that the positions of the front and rear vehicles and the vehicle velocity have an influence on the onset location of covering the brake pedal. Structural equation modeling (SEM) was applied to estimate these relationships quantitatively. The results imply that the SEM with latent variables can represent the hypotheses obtained from the analysis of the measured data. We propose a detection method of unusual driver behavior by predicting the drivers preparatory behavior using the SEM, and possible new application of in-vehicle navigation systems is discussed.

Identifying Factors Related to the Estimation of Near-Crash Events of Elderly Drivers

This study attempted to identify factors associated with driving behavior of elderly drivers to assess their safety and estimate their risk during naturalistic driving. We performed binomial logistic regression using self-reported past crash involvement as a response variable to identify critical factors and provided an estimation model has 18 variables. However, applying driver category based on crash and near-crash events (CNCs) collected from naturalistic driving study employed on-dash cam instead of self-reported crash involvement to the previous model showed lower predictive performance (0.63 for sensitivity and 0.51 for specificity). This implies that the model based on self-reported crash experiences was difficult to detect for drivers with CNC during naturalistic driving. Then, we performed binomial logistic regression based on CNC involvement and indicated another model, where the predictive performance was improved, with 0.81 for sensitivity and 0.70 for specificity. To predict the number of CNCs as drivers’ risk, this study adopted Poisson regression analysis using nine variables selected from the second model. The analyses showed a plausible model and significant variables for the estimation of CNCs. Mini-Mental State Examination (MMSE) was one of the better predictor putting in this model, and showed the probability that lower performance associated with higher number of CNCs. This model for CNC estimation would be helpful for the development of safety programs for elderly drivers with possible incidents.

Electrophysiological assessment of driving pleasure and difficulty using a task-irrelevant probe technique

The amplitude of event-related brain potentials (ERPs) elicited by task-irrelevant auditory probes decreases when more attentional resources are allocated to a visual task. This task-irrelevant probe technique is considered to be useful in assessing the degree of interest in a visual task, as well as task difficulty. The present study examined the amplitude of the N1 and P2 components elicited by task-irrelevant auditory probes during a driving task in a simulated environment. The analysis of ERPs showed that the N1 amplitude decreased when participants drove on the road course that had more frequent and sharper curves, whereas the P2 amplitude decreased when the road contained sharper curves, irrespective of curve frequency. Subjective ratings of driving pleasure and difficulty showed the same variation patterns as the N1 and P2 amplitudes, respectively. These results suggest that use of the task-irrelevant probe technique can assess the degree of driving pleasure and difficulty separately.

Driving simulator study of driver behavior while using head-up display

We developed head-up display (HUD) in the AIST driving simulator. The information on the HUD is displayed on the front screen using an additional projector that is mounted on the roof of the vehicle cabin. The projection range covers the entire windshield. We evaluated drivers acceptance of the HUD that is presented around a location of a leading vehicle. The findings indicate that drivers paid more attention to the information overlapping the lead vehicle. The attentional workload was improved by the information location and information contents.