<div class="section abstract"><div class="htmlview paragraph">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). To construct a highly accurate model, we analyzed critical variables as input information affecting the output of the LSTM model using a random forest (RF) model. The results showed the high importance of the EGV velocity, THW, and relative velocity as information related to the traveling lane, in addition to the CIV velocity as information related to the CIV. The CIV data obtained after the lane change were used for the PRV in this analysis. Based on the variables analyzed in the RF model, we constructed personalized driver models using LSTM, and the mean coefficient of determination was greater than 0.95, indicating that this system is more accurate than the conventional car-following models. The driver models constructed in this study are expected to improve the usability of AVSs employing the driver model.</div></div>
<div class="section abstract"><div class="htmlview paragraph">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). The road geometry of an actual section of the Tomei Expressway, which comprises straight and curved roads, between Tokyo and Nagoya in Japan was simulated in the DS. Personalized driver models were constructed using LSTM based on the data of driving maneuvers on the expressway. Simulation results indicate that model accuracy decreases for the entire experimental road compared to that for each curved road; the model accuracy of each curved road was improved. In order to improve the accuracy, it is effective to build a model for each curve or section, and the accuracy is lower at the exit than at the entrance of the curve, and highest at the middle. And then it is necessary to consider both the time required to improve accuracy and the change in curvature when considering the construction of personalized models on curved roads.</div></div>
Level 3 automated driving requires a driver to operate a vehicle in response to a takeover request according to the Society of Automotive Engineers; therefore, the driver should maintain a state of wakefulness. The study aimed to evaluate and verify whether the presented stimuli, i.e., saccade-inducing stimuli (SaS) as visual stimuli, scent stimuli (ScS) as olfactory stimuli, and a combination of saccade-inducing and scent stimuli (CoS), can maintain the wakeful state of a driver. A driving simulator (DS) that can reproduce the actual movement of a motor vehicle using a turn table and six-axis motion device is used to simulate driving under four different conditions: no stimulation, SaS, ScS, and CoS. The percentage of eye closure (PERCLOS) and the electroencephalogram (EEG) analysis were used for the quantitative evaluation, while the participants were subjected to subjective evaluation. The results showed that the average increase in arousal time for each condition was 30.2, 53.7, and 82.3% for the SaS, ScS, and CoS cases, respectively. The average reduction rate in the EEG readings was 39.9, 41.0, and 71.4% for the SaS, ScS, and CoS cases, respectively. All participants indicated that the CoS had the highest effect of arousal as a driver stimulation tool.
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