Toshihiro Wakita

Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification

All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following distance and velocity mapped into a two-dimensional space is modeled for each driver with an optimal velocity model approximated by a nonlinear function or with a statistical method of a Gaussian mixture model (GMM). Pedal operation patterns are also modeled with GMMs that represent the distributions of raw pedal operation signals or spectral features extracted through spectral analysis of the raw pedal operation signals. The driver models are evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle. Experimental results show that the driver model based on the spectral features of pedal operation signals efficiently models driver individual differences and achieves an identification rate of 76.8% for a field test with 276 drivers, resulting in a relative error reduction of 55% over driver models that use raw pedal operation signals without spectral analysis

On the Use of Stochastic Driver Behavior Model in Lane Departure Warning

In this paper, we propose a new framework for discriminating the initial maneuver of a lane-crossing event from a driver correction event, which is the primary reason for false warnings of lane departure prediction systems (LDPSs). The proposed algorithm validates the beginning episode of the trajectory of driving signals, i.e., whether it will cause a lane-crossing event, by employing driver behavior models of the directional sequence of piecewise lateral slopes (DSPLS) representing lane-crossing and driver correction events. The framework utilizes only common driving signals and allows the adaptation scheme of driver behavior models to better represent individual driving characteristics. The experimental evaluation shows that the proposed DSPLS framework has a detection error with as low as a 17% equal error rate. Furthermore, the proposed algorithm reduces the false-warning rate of the original lane departure prediction system with less tradeoff for the correct prediction.

Driver Identification Using Driving Behavior Signals

In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of the accelerator pedal, brake pedal, vehicle velocity, and distance from the vehicle in front, are measured using a driving simulator. We compared the identification rate obtained using different identification models and different features. As a result, we found the nonparametric models is better than the parametric models. Also, the drivers operation signals were found to be better than road environment signals and car behavior signals. The identification rate for thirty driver using actual vehicle driving in a city area was 73%.

Driver identification using driving behavior signals

In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of the accelerator pedal, brake pedal, vehicle velocity, and distance from the vehicle in front, are measured using a driving simulator. We compared the identification rate obtained using different identification models and different features. As a result, we found the nonparametric models is better than the parametric models. Also, the drivers operation signals were found to be better than road environment signals and car behavior signals. The identification rate for thirty driver using actual vehicle driving in a city area was 73%.

Self-Coaching System Based on Recorded Driving Data: Learning From One's Experiences

This paper describes the development of a self-coaching system to improve driving behavior by allowing drivers to review a record of their own driving activity. By employing stochastic driver-behavior modeling, the proposed system is able to detect a wide range of potentially hazardous situations, which conventional event data recorders are not able to capture, including those involving latent risks, of which drivers themselves are unaware. By utilizing these automatically detected hazardous situations, our web-based system offers a user-friendly interface for drivers to navigate and review each hazardous situation in detail (e.g., driving scenes are categorized into different types of hazardous situations and are displayed with corresponding multimodal driving signals). Furthermore, the system provides feedback on each risky driving behavior and suggests how users can safely respond to such situations. The proposed system establishes a cooperative relationship between the driver, the vehicle, and the driving environment, leading to the development of the next generation of safety systems and paving the way for an alternative form of driving education that could further reduce the number of fatal accidents. The systems potential benefits are demonstrated through preliminary extensive evaluation of an on-road experiment, showing that safe-driving behavior can be significantly improved when drivers use the proposed system.

Parametric versus non-parametric models of driving behavior signals for driver identification

In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of of the accelerator pedal, brake pedal, vehicle velocity, and distance from the vehicle in front, are measured using a driving simulator. We compared the identification rate obtained using different identification models and different features. As a result, we found the non-parametric models to be better than the parametric models. Also, the drivers operation signals were found to be better than road environment signals and car behavior signals.

Development of a new breath alcohol detector without mouthpiece to prevent alcohol-impaired driving

A new breath alcohol detector for a driver has been developed. A mouthpiece is not required for the detection because driverpsilas breath sample is captured by an electric suction fan of the detector. The influence of an arbitrary dilution of driverpsilas expiration is extremely reduced by the calibration of alcohol concentration, using an oxygen level of driverpsilas expired breath that is measured simultaneously with the alcohol content. The detector is able to measure breath alcohol concentration rapidly and easily, compared with the current breath alcohol detectors, which require a blowing through a mouthpiece. Good accuracy has been demonstrated in an experiment with the drunk subjects.

Improving driving behavior by allowing drivers to browse their own recorded driving data

In this paper, we present our on-going efforts to develop a self-diagnosis system to improve safe-driving behavior by allowing drivers to review a record of their own driving activity. By employing stochastic driver-behavior modeling, the proposed system is able to detect various types of potentially hazardous situations, which conventional event data recorders are not able to capture, or which drivers themselves are not aware of such latent risky situations. Utilizing these automatically detected hazardous situations, our web-based system offers a user-friendly interface for drivers to navigate and review each hazardous situation in detail (e.g., driving scenes are displayed with corresponding driving signals). Furthermore, the system provides feedback on each risky driving behavior and suggests how the users can appropriately respond to such situations in a safe manner. The proposed system establishes a cooperative relationship between the driver, the vehicle, and the driving environment in order to develop the next-generation of safety systems and pave the way for an alternative form of driver education that could further reduce the number of fatal accidents. The systems potential benefits are demonstrated through experimental evaluation, showing that safe-driving behavior improved significantly after using the proposed system.

Slow eye movement as a possible predictor of reaction delays to auditory warning alarms in a drowsy state

In recently developed intelligent vehicles, warning alarms are often used to prompt avoidance behaviours from drivers facing imminent hazardous situations. However, when critical reaction delays to auditory stimulation are anticipated, the alarm should be activated earlier to compensate for such delays. It was found that reaction times to an auditory stimulus significantly increased in the presence of slow eye movement (SEM), which is known to occur frequently during the wake–sleep transition. The reaction delay could not be attributed to temporal effects such as fatigue and was invariant regardless of response effectors (finger or foot). Moreover, it was found that applied pedal force decreased immediately after an auditory stimulus interrupted SEM. Consequently, it was concluded that SEM can be a good predictor of reaction delays to auditory warning alarms when drivers are in a drowsy state. Statement of Relevance:The present study demonstrated that simple auditory reaction time significantly increased when SEM emerged. In the design of vehicle safety systems using warning alarms to prompt avoidance behaviours from drivers, such reaction delays during SEM must be taken into account.

Generating lane-change trajectories of individual drivers

This paper describes a method to generate vehicle trajectories of lane change paths for individual drivers. Although each driver has a consistent preferance in the lane change behavior, lane-changing time and vehicle trajectory are uncertain due to the presence of surrounding vehicles. To model this uncertainty, we propose a statistical driver model. We assume that a driver plans various vehicle trajectories depending on the surrounding vehicles and then selects a safe and comfortable trajectory. Lane change patterns of each driver are modeled with a hidden Markov model (HMM), which is trained using longitudinal vehicle velocity, lateral vehicle position, and their dynamic features. Vehicle trajectories are generated from the HMM in a maximum likelihood criterion at random lane-changing time and state duration. Experimental results show that vehicle trajectories generated from the HMM included a similar trajectory to that of a target driver.