Katsunobu Itou

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

Jijo-2: an office robot that communicates and learns

Describes how the authors have combined speech recognition, dialogue management, and statistical learning procedures to develop Jijo-2; an office robot that can communicate with humans and learn about its environment.

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.

Galatea: Open-Source Software for Developing Anthropomorphic Spoken Dialog Agents

Galatea is a software toolkit to develop a human-like spoken dialog agent. In order to easily integrate the modules of different characteristics including speech recognizer, speech synthesizer, facial animation synthesizer, and dialog controller, each module is modeled as a virtual machine having a simple common interface and connected to each other through a broker (communication manager). Galatea employs model-based speech and facial animation synthesizers whose model parameters are adapted easily to those for an existing person if his or her training data is given. The software toolkit that runs on both UNIX/Linux and Windows operating systems will be publicly available in the middle of 2003 [7, 6].

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%.

Generation of Pedal Operation Patterns of Individual Drivers in Car-Following for Personalized Cruise Control

This paper presents a method to generate car-following patterns for individual drivers. We assume that driving is a recursive process. A driver recognizes a road environment such as velocity and following distance and adjusts gas and brake pedal positions. A vehicle status changes according to the drivers operation and the road environment changes according to the vehicle status. Driving patterns of each driver are modeled with a Gaussian mixture model (GMM), which is trained as a joint probability distribution of following distance, velocity, pedal position signals and their dynamics. Gas and brake pedal operation patterns are generated from the GMMs in a maximum likelihood criterion so that the conditional probability is maximized for a given environment i.e., following distance and velocity. Experimental results for a driving simulator show that car-following patterns generated from GMMs for three different drivers maintain their individual driving characteristics.

Adaptive Nonlinear Regression Using Multiple Distributed Microphones for In-Car Speech Recognition

In this paper, we address issues in improving hands-free speech recognition performance in different car environments using multiple spatially distributed microphones. In the previous work, we proposed the multiple linear regression of the log spectra (MRLS) for estimating the log spectra of speech at a close-talking microphone. In this paper, the concept is extended to nonlinear regressions. Regressions in the cepstrum domain are also investigated. An effective algorithm is developed to adapt the regression weights automatically to different noise environments. Compared to the nearest distant microphone and adaptive beamformer (Generalized Sidelobe Canceller), the proposed adaptive nonlinear regression approach shows an advantage in the average relative word error rate (WER) reductions of 58.5% and 10.3%, respectively, for isolated word recognition under 15 real car environments.

On-Going Data Collection of Driving Behavior Signals

We are developing a large-scale real-world driving database of more than 200 drivers using a data collection vehicle equipped with various sensors for the synchronous recording of multimedia data including speech, video, driving behavior, and physiological signals. Driver’s speech and videos are captured with multi-channel microphones and cameras. Gas and brake pedal pressures, steering angles, vehicle velocities, and following distances are measured using pressure sensors, a potentiometer, a velocity pulse counter, and distance sensors, respectively. Physiological sensors are mounted to measure driver’s heart rate, skin conductance, and emotion-based sweating. The multimedia data is collected under four different task conditions while driving on urban roads and an expressway. Data collection is currently underway and to date 151 drivers have participated in the experiment. Data collection is being conducted in international collaboration with the United States and Europe. This chapter reports on our on-going driving data collection in Japan.

An online customizable music retrieval system with a spoken dialogue interface

In this paper, we introduce a spoken language interface for music information retrieval. In response to voice commands, the system searches for a song through an internet music shop or a ‘‘playlist’’ stored in the local PC; the system then plays it. To cope with the almost unlimited size of the vocabulary, a remote server program with which a user can customize their recognition grammar and dictionary is implemented. When a user selects favorite artists, the server program automatically generates a minimal set of recognition grammars and a dictionary. The system then sends them to the interface program. Therefore, on average, the vocabulary is less than 1000 words for each user. To perform a field test of the system, we implemented a speech collection capability, whereby speech utterances are compressed in free lossless audio codec (FLAC) format and are sent back to the server program with dialogue logs. Currently, the system is available to the public for experimental use. More than 100 users are involve...

Modeling of individualities in driving through spectral analysis of behavioral signals

Driving behavior modeling using such driving signals as velocity, following distance, and gas or brake pedal operations, has been investigated for accident prevention and vehicle design. Driving behaviors are different among drivers, and research on driver modeling has also been carried out from different points of view in cognitive and engineering approaches. In this paper, driver’s characteristics in driving behaviors are modeled with a Gaussian mixture model (GMM) using “cepstral features” obtained through spectral analysis of gas pedal operation signals. The GMM driver model based on cepstral features is evaluated in driver identification experiments and compared with a conventional GMM driver model that uses raw driving signals without spectral analysis. Experimental results show that the proposed driver model achieves an 89.6% driver identification rate, resulting in 61% error reduction over the conventional driver model.