Hirotoshi Iwasaki

Evaluation Model of Cognitive Distraction State Based on Eye Tracking Data Using Neural Networks

Eye tracking reveals a persons state of mind. Thus, representing personal cognitive states using eye tracking leads to objective evaluations of these states, and this can be applied to various fields. In this paper, we propose a model that evaluates the degree of personal distraction based on eye tracking. Moreover, we apply the proposed model to eye tracking for a person driving a car.

Qualitative Reasoning Approach to a Driver's Cognitive Mental Load

This paper explores applying qualitative reasoning to a drivers mental state in real driving situations so as to develop a working load for intelligent transportation systems. The authors identify the cognitive state that determines whether a driver will be ready to operate a device in car navigation. In order to identify the drivers cognitive state, the authors will measure eye movements during car-driving situations. Data can be acquired for the various actions of a car driver, in particular braking, acceleration, and steering angles from the experiment car. The authors constructed a driver cognitive mental load using the framework of qualitative reasoning. The response of the model was checked by qualitative simulation. The authors also verified the model using real data collected by driving an actual car. The results indicated that the model could represent the change in the cognitive mental load based on measurable data. This means that the framework of this paper will be useful for designing user interfaces for next-generation systems that actively employ user situations.

Pheromone model: application to traffic congestion prediction

Social insects perform complex tasks without top-down style control, by sensing and depositing chemical markers called “pheromone”. We have examined applications of this pheromone paradigm towards intelligent transportation systems (ITS). Many of the current traffic management approaches require central processing with the usual risk for overload, bottlenecks and delays. Our work points towards a more decentralized approach that may overcome those risks. In this paper, a car is regarded as a social insect that deposits (electronic) pheromone on the road network. The pheromone represents density of traffic. We propose a method to predict traffic congestion of the immediate future through a pheromone mechanism without resorting to the use of a traffic control center. We evaluate our method using a simulation based on real-world traffic data and the results indicate applicability to prediction of immediate future traffic congestion. Furthermore, we describe the relationship between pheromone parameters and accuracy of prediction.

Evaluating significance of historical entities based on tempo-spatial impacts analysis using Wikipedia link structure

We propose a method to evaluate the significance of historical entities (people, events, and so on.). Here, the significance of a historical entity means how it affected other historical entities. Our proposed method first calculates the tempo-spacial impact of historical entities. The impact of a historical entity varies according to time and location. Historical entities are collected from Wikipedia. We assume that a Wikipedia link between historical entities represents an impact propagation. That is, when an entity has a link to another entity, we regard the former is influenced by the latter. Historical entities in Wikipedia usually have the date and location of their occurrence. Our proposed iteration algorithm propagates such initial tempo-spacial information through links in the similar manner as PageRank, so the tempo-spacial impact scores of all the historical entities can be calculated. We assume that a historical entity is significant if it influences many other entities that are far from it temporally or geographically. We demonstrate a prototype system and show the results of experiments that prove the effectiveness of our method.

Applying qualitative reasoning to a driver's cognitive mental load

In this paper, we explore applying qualitative reasoning to a drivers mental state in real driving situations so as to develop a working load for intelligent transportation systems. We identify the cognitive state that determines whether a driver will be ready to operate a device in car navigation. In order to identify the drivers cognitive state, we will measure eye movements during car-driving situations. We can acquire data for the various actions of a car driver, in particular braking, acceleration, and steering angles from our experiment car. We constructed a driver cognitive mental load using the framework of qualitative reasoning. We checked the response of our model by qualitative simulation. We also verified the model using real data collected by driving an actual car. The results indicated that our model could represent the change in the cognitive mental load based on measurable data. This means that the framework of this paper will be useful for designing user interfaces for next-generation systems that actively employ user situations.

User-Adapted Car Navigation System using a Bayesian Network -Personalized Recommendation of Content

Recent car navigation systems now provide more content than ever. However, retrieving and selecting such content poses safety issues to users, especially drivers. Moreover, usability issues arise from simple user interfaces. Thus, it is important for the system to recommend content adapted to the users preferences and situations automatically. In this paper, we analyze the validity of applying a Bayesian network to a user preference model of a content recommendation system in cars. We also present a practical way of building models using an information criterion as well as domain knowledge and an incremental learning method to adapt to individual users.

A new approach to detecting distracted car drivers using eye-movement data

In our study, we generate new rules for determining whether or not a driver is distracted, using collected data about the drivers eye movement and driving data by learning as a new approach to detecting distracted car drivers. We use a learning tool, namely a support vector machine (SVM), to generate the rules. In addition, we focused on a qualitative model of a drivers cognitive mental load in a prior study and investigated the relationship between this model and the drivers distraction. In the investigation, we verify drivers eye movements and driving data that are inconsistent with the model.

Classifying Cognitive Load and Driving Situation with Machine Learning

 Abstract—This paper classifies a drivers cognitive state in real driving situations to improve the in-vehicle information service that judges a users cognitive load and driving situation. We measure the drivers eye movement and collect driving sensor data such as braking, acceleration, and steering angles that are used to classify the drivers state. A set of data about the drivers degree of cognitive load, regarded as a training set, is obtained from steering operation and task cognition. Given such information, we use a machine-learning method to classify the drivers cognitive load. We achieved reasonable accuracy in certain driving situations in which the driver moves abnormally for an appropriate service supporting safe driving. surrounding situation, the range of eye movement may increase because the drivers cognitive load has increased. This means that a high cognitive load and a change of eye movement are related. Eye movement is used in the field of physiological psychology for clarifying control (1). It is directly related to perception and can be considered an indication of mental load. Driving a car requires prediction of the surrounding environment and is influenced by the situation. Therefore, a users cognitive load can be classified using these features. To do this, we measure the drivers eye movement and gather driving data such as accelerator use, braking, and steering. This paper takes a machine-learning approach to the above cognitive state identification problem in a realistic car-driving task. We set up cognitive loads according to the steering-entropy method (2) and a definition of the task cognition situation. This paper is organized as follows. Section II presents related works. Section III defines cognitive load. Section IV describes our classification model for cognitive load. Section V presents a performance evaluation. The final section provides conclusions.

Evaluation model of cognitive distraction state based on eye-tracking data using neural networks

Eye tracking reveals a persons state of mind. Thus, representing personal cognitive states using eye tracking leads to objective evaluations of these states, and this can be applied to various fields. In this paper, we propose a model that evaluates the degree of personal distraction based on eye tracking. Moreover, we apply the proposed model to eye tracking for a person driving a car.

Designing a Car-Driver's Cognitive Process Model for considering Degree of Distraction

A distracted state of a driver affects car driving state. The eye tracking can reveal an individuals psychological state. In this paper, we design a drivers cognitive process model by clearly indicating the relations between cognitive states, such as perception and memory, in the process to produce the driving action using the eye tracking data. It is important to consider degree of distraction. Therefore, we consider a cognitive distraction expressed both serially and quantitatively in the model. In this modeling, we utilize a production system framework, and the cognitive distracted state is managed by a module in the production system.