Naiwala P. Chandrasiri

Semiotic prediction of driving behavior using unsupervised double articulation analyzer

In this paper, we propose a novel semiotic prediction method for driving behavior based on double articulation structure. It has been reported that predicting driving behavior from its multivariate time series behavior data by using machine learning methods, e.g., hybrid dynamical system, hidden Markov model and Gaussian mixture model, is difficult because a drivers behavior is affected by various contextual information. To overcome this problem, we assume that contextual information has a double articulation structure and develop a novel semiotic prediction method by extending nonparametric Bayesian unsupervised morphological analyzer. Effectiveness of our prediction method was evaluated using synthetic data and real driving data. In these experiments, the proposed method achieved long-term prediction 2-6 times longer than some conventional methods.

Development of pedestrian behavior model taking account of intention

In order for robots to safely move in human-robot coexisting environment, they must be able to predict their surrounding peoples behavior. In this study, a pedestrian behavior model that produces humanlike behavior was developed. The model takes into account the pedestrians intention. Based on the intention, the model pedestrian sets its subgoal and moves toward the subgoal according to virtual forces affected by other pedestrian and environment. The proposed model was verified through pedestrian observation experiments.

Cyber physical system for vehicle application

In-vehicle information provision services have started according to the progress of data communication infrastructure surrounding vehicles. In such information services, a large amount of data related to vehicles and drivers has been accumulated to the data-center and also been analyzed to provide proper information to drivers. Towards such a technical trends surrounding vehicles, a cyber physical system for vehicle application is proposed here. In the proposed system, expected continuous spiral information flow for vehicles and drivers is described. In the data-center, accumulated information has been analyzed by intelligent information processing of data-mining, then finally extracted information has been provided to drivers through human machine interface. According to the progress of the information processing technologies in the data-center, several potential application are introduced mainly based on personal adaptation, big data analysis and web mining.

Sequence Prediction of Driving Behavior Using Double Articulation Analyzer

A sequence prediction method for driving behavior data is proposed in this paper. The proposed method can predict a longer latent state sequence of driving behavior data than conventional sequence prediction methods. The proposed method is derived by focusing on the double articulation structure latently embedded in driving behavior data. The double articulation structure is a two-layer hierarchical structure originally found in spoken language, i.e., a sentence is a sequence of words and a word is a sequence of letters. Analogously, we assume that driving behavior data comprise a sequence of driving words and a driving word is a sequence of driving letters. The sequence prediction method is obtained by extending a nonparametric Bayesian unsupervised morphological analyzer using a nested Pitman-Yor language model (NPYLM), which was originally proposed in the natural language processing field. This extension allows the proposed method to analyze incomplete sequences of latent states of driving behavior and to predict subsequent latent states on the basis of a maximum a posteriori criterion. The extension requires a marginalization technique over an infinite number of possible driving words. We derived such a technique on the basis of several characteristics of the NPYLM. We evaluated this proposed sequence prediction method using three types of data: 1) synthetic data; 2) data from test drives around a driving course at a factory; and 3) data from drives on a public thoroughfare. The results showed that the proposed method made better long-term predictions than did the previous methods.

Route recommendation method for car navigation system based on estimation of driver's intent

Nowadays, car navigation systems are widely used in cars to aid drivers by providing directions to a destination. However, these systems do not always recommend a route that perfectly matches the drivers intent. Even when drivers intentionally select a route that is different from the recommended one, the system leads them back to the original route. Such recommendations do not adequately reflect the drivers intent. This study proposes a route recommendation method for a car navigation system that estimates the drivers intent and rerecommends a route that matches this intent when the driver deviates from the originally recommended route. We developed a simulator based on the proposed method and used it to experimentally verify the effectiveness of the proposed method.

Driving skill analysis using machine learning The full curve and curve segmented cases

Analysis of driving skill/driver state can be used in building driver support and infotainment systems that can be adapted to individual needs of a driver. In this paper we present a machine learning approach to analyzing driving maneuver skills of a driver that covers both longitudinal and lateral controls. The concept is to learn a driver model from sensor data that are related to driving environment, driving behavior and vehicle response. Once the model is built, driving skills of an unknown run can be classified automatically. In this paper, we demonstrate the feasibility of the proposed method for driving skill analysis based on a driving simulator experiment in a curve driving scene for both the full curve and curve segmented cases.

Collaborative exploratory search in real-world context

We propose Collaborative Exploratory Search (CES), which is an integration of dialog analysis and web search that involves multiparty collaboration to accomplish an exploratory information retrieval goal. Given a real-time dialog between users on a single topic; we define CES as the task of automatically detecting the topic of the dialog and retrieving task-relevant web pages to support the dialog. To recognize the task of the dialog, we apply the Author--Topic model as a topic model. Then, attribute extraction is applied to the dialog to obtain the attributes of the tasks. Finally, a specific search query is generated to identify the task-relevant information. We implement and evaluate the CES system for a commercial in-vehicle conversation. We also develop an iPad application that listens to conversations among users and continuously retrieves relevant web pages. Our experimental results reveal that the proposed method outperforms existing methods, which demonstrates the potential usefulness of collaborative exploratory search with practically usable accuracy levels.

Information Analysis and Natural Presentation Based on a Cyber-Physical System for Automobiles

In the proposed system, the authors describe expected continuous spiral information flow for vehicles and drivers. In the data center, intelligent information processing technology analyzes accumulated information, and then a human-machine interface presents trusted information to drivers. The authors introduce several potential applications, mainly based on personal adaptation and Big Data analysis. They also introduce a tactile sense device, as an experimental challenge to presenting instructions in a natural, intuitive manner.

A method of structuring communication data for in-vehicle information service

New in-vehicle information provision services have started according to the progress of data communication infrastructure surrounding vehicles, where the vehicles can connect to communication network outside. In such information services, a large amount of various data related to vehicles and/or drivers will be stored to the datacenter over the network in near future. Data and functions which are currently equipped with in-vehicle devices will be shifted to the datacenter, and these data and functions will be delivered to each of vehicles from the data center in need. In this paper, we propose a method of structuring such data based on ontological engineering approach for the purpose of providing drivers with useful information utilizing a large amount of communication data effectively. That is, we introduce a mechanism of providing drivers with information effectively, using the data structure which refers to ontology as a data schema. Also we describe about the information space with the contents of information to provide and the driver model with ontology.

A System for Generating Background Color of E-Book based on Text of a Novel

近年,スマートフォン・タブレット端末・電子書籍リー ダーなどによる電子書籍が普及してきている.それに伴い 小説を電子書籍で読む機会も増えてきている.電子書籍は 実際に本を所持することなく多くの書籍をデータとして持 ち歩くことができるのが利点である.しかし,これらの端 末はカラー画像や動画・音声等を表現できる機能を有して いるにも関わらず,従来の電子書籍アプリケーションでは 紙媒体の本と同様に背景色が白,文字色が黒という場合が 多い.これではスマートフォン・タブレット端末の持つ表 現能力やインタフェースを充分活かしきれているとは言え ず,電子書籍の利点は限定的である. 電子書籍はWebサイトやSNSと違い,充分に校閲され整 然とした文章で構築されているため,自然言語処理との相 性が良い.自然言語文と映像の生成を組み合わせる分野と してはweb画像の分類を行うことを目的とした画像の説明 文を生成するシステムの提案がなされている1)2).これら は映像から自然言語文を生成する取り組みである.これら とは逆の自然言語文から画像を出力する研究としては,湊 ら3)による俳句に含まれる名詞句の季語から画像を生成し, 俳句の情景に適合した画像を生成するシステムの構築が挙 げられる.また菅生ら4)は風景描写文から主題と連想語を抽 出し,単語単体ではなく単語同士の想起度を計算し,想起 度の高い単語からそれに合った画像オブジェクトを配置す ることで風景画像の生成を行うシステムの提案をしている. 色は感性に与える影響において大きな役割を持つ要素であ り,背景色によって文字の読みやすさや商品のイメージが変 わるなどの研究が背景色に着目して行われてきている5)~7). ただし,書籍に関しては読みやすい色の設定という部分に 重きを置くもので,実際の文章の中身と合致させるという 研究ではない.また,この背景色という考えを電子書籍へ 適用した例は少ない.表現の付加を行う研究として吉田ら8) は感情タグを文章に併記することでその感情を基にリー ダーアプリケーションの背景色を変化させることを試み た.しかし,このシステムでは人手で小説文章に対し喜び, 怒り等のタグ付けが必要である.これに対し,本研究では 自動で背景色を生成する手法を考案する.島田ら8)は挿絵 を付加することで,読者の文章理解を向上させる研究を 行っているが,挿絵や背景画像を付加する場合,不適切な 画像か考慮する仕組みや文字と重なることでの見易さを考 慮する必要がある.田村ら9)の研究においては,コンテン ツへのエフェクト追加による印象の変化と閲覧体験の向上 の有用性を示しつつも,テキストへのエフェクトの付与は 見易さを悪化させる問題点があったため,文字の読みやす さを維持しつつ読書体験を向上させることが重要だと考え る.したがって,本研究では従来の電子書籍アプリケー ションの表示システムと大きく変わることなく読書体験の 向上を図ったため「単色の背景色」とした.今回は文章の表 現している情景に着目し,文章に含まれる名詞句から背景 色を作製する処理を電子書籍アプリケーションに実装し た.これにより,あらゆる電子書籍に対し自動で背景色を 生成されるため,読者が場面の情景をしやすく,より物語 あらまし 近年,スマートフォンやタブレット端末・電子書籍リーダーといった電子端末で利用できる電子書籍が 普及してきている.それに伴い小説を電子書籍で読む機会も増えているが,従来の電子書籍アプリケーションは紙媒 体の本と同様に背景色が白,文字色が黒という場合が多く,電子書籍がディジタル端末で展開されている利点を充分 活かしきれているとは言えない.そこで本研究では,小説文章に含まれる単語から小説文章の情景に適した背景色を 生成し表示するシステムを開発した.これにより電子書籍端末の持つ表現力を充分に活用し読者へより場面の情景を 伝え,感情移入を促す効果を実現した.また,16人の実験協力者を対象に実際に構築した電子書籍アプリケーション の評価実験を行った.その結果,読みやすさについて改善の必要があるものの,感情移入のしやすさにおいて従来の 表示形式の評価を上回る結果となり,本システムの読書体験における有用性が確認できた.