Chiyomi Miyajima

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

Biometric identification using driving behavioral signals

We investigate the uniqueness of driver behavior in vehicles and the possibility of using it for personal identification with the objectives of achieving safer driving, of assisting the driver in case of emergencies, and of being a part of a multi-mode biometric signature for driver identification. We use Gaussian mixture models (GMM) for modeling the individualities of the accelerator and brake pedal pressures, and focus on not only the static features, but also the dynamics of the pedal pressures. Experimental results show that the dynamic features significantly improve the performance of driver identification.

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%.

International Large-Scale Vehicle Corpora for Research on Driver Behavior on the Road

This paper considers a comprehensive and collaborative project to collect large amounts of driving data on the road for use in a wide range of areas of vehicle-related research centered on driving behavior. Unlike previous data collection efforts, the corpora collected here contain both human and vehicle sensor data, together with rich and continuous transcriptions. While most efforts on in-vehicle research are generally focused within individual countries, this effort links a collaborative team from three diverse regions (i.e., Asia, American, and Europe). Details relating to the data collection paradigm, such as sensors, driver information, routes, and transcription protocols, are discussed, and a preliminary analysis of the data across the three data collection sites from the U.S. (Dallas), Japan (Nagoya), and Turkey (Istanbul) is provided. The usability of the corpora has been experimentally verified with a Cohens kappa coefficient of 0.74 for transcription reliability, as well as being successfully exploited for several in-vehicle applications. Most importantly, the corpora are publicly available for research use and represent one of the first multination efforts to share resources and understand driver characteristics. Future work on distributing the corpora to the wider research community is also discussed.

A Study of Driver Behavior Under Potential Threats in Vehicle Traffic

Although, in recent years, significant developments have been made in road safety, traffic statistics indicate that we still need significant improvements in the field. Since traffic accidents usually reflect human factors, in this paper, we focus on clarifying the understanding of driver behaviors under hazardous scenarios. Brake pedal signals or driver speech, or both, are utilized to detect incidents from a real-world driving database of 373 drivers. Results are then analyzed to address the individuality in driver behaviors, the multimodality of driver reactions, and the detection of potentially dangerous locations. All of the existing 25 potentially hazardous scenes in the database are hand labeled and categorized. Based on the joint histograms of behavioral signals and their time derivatives, a detection feature is proposed and satisfactorily applied to the indication of anomalies in driving behavior. Seventeen scenes, where a reaction utilizing the brake pedal was observed, are detected with a true positive (TP) rate of 100% and a false positive (FP) rate of 4.1%. We demonstrate the relevance of considering behavior individuality. During 11 scenes, the drivers verbally reacted. Scenes that included high-energy words are adequately detected by the speech-based method, which achieved a TP rate of 54% for an FP rate of 6.4%. The integration of different behavior modalities satisfactorily boosts the detection of the most subjectively hazardous situations, which suggests the importance of considering multimodal reactions. Finally, a strong relationship is presented between locations where potentially hazardous situations occurred and areas of frequent strong braking.

Analysis of Real-World Driver's Frustration

This paper investigates a method for estimating a drivers spontaneous frustration in the real world. In line with a specific definition of emotion, the proposed method integrates information about the environment, the drivers emotional state, and the drivers responses in a single model. Driving data are recorded using an instrumented vehicle on which multiple sensors are mounted. While driving, drivers also interact with an automatic speech recognition (ASR) system to retrieve and play music. Using a Bayesian network, we combine knowledge on the driving environment assessed through data annotation, speech recognition errors, the drivers emotional state (frustration), and the drivers responses measured through facial expressions, physiological condition, and gas- and brake-pedal actuation. Experiments are performed with data from 20 drivers. We discuss the relevance of the proposed model and features of frustration estimation. When all of the available information is used, the overall estimation achieves a true positive rate of 80% and a false positive rate of 9% (i.e., the system correctly estimates 80% of the frustration and, when drivers are not frustrated, makes mistakes 9% of the time).

Development of VAD evaluation framework CENSREC-1-C and investigation of relationship between VAD and speech recognition performance

Voice activity detection (VAD) plays an important role in speech processing including speech recognition, speech enhancement, and speech coding in noisy environments. We developed an evaluation framework for VAD in such environments, called corpus and environment for noisy speech recognition 1 concatenated (CENSREC-1-C). This framework consists of noisy continuous digit utterances and evaluation tools for VAD results. By adoptiong two evaluation measures, one for frame-level detection performance and the other for utterance-level detection performance, we provide the evaluation results of a power-based VAD method as a baseline. When using VAD in speech recognizer, the detected speech segments are extended to avoid the loss of speech frames and the pause segments are then absorbed by a pause model. We investigate the balance of an explicit segmentation by VAD and an implicit segmentation by a pause model using an experimental simulation of segment extension and show that a small extension improves speech recognition.

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%.

A new approach to designing a feature extractor in speaker identification based on discriminative feature extraction

This paper presents a new framework for designing a feature extractor in a speaker identification system based on the discriminative feature extraction (DFE) method. In order to find the frequency scale appropriate for accurate speaker identification, a mel-cepstral estimation technique using a second-order all-pass warping function is applied to the feature extractor; the frequency warping parameters and the text-independent speaker model parameters are jointly optimized based on a minimum classification error (MCE) criterion. Experimental results show that the frequency scale after optimization is different from traditional Linear/Mel scales and the proposed system outperforms conventional systems in which only the classifier is optimized with the MCE criterion.

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.