Skip to:Content
|
Bottom
Cover image for In-vehicle corpus and signal processing for driver behavior
Title:
In-vehicle corpus and signal processing for driver behavior
Publication Information:
New York : Springer, 2008
Physical Description:
xv, 247 p. : ill. some col. ; 24 cm.
ISBN:
9780387795812
Added Author:

Available:*

Library
Item Barcode
Call Number
Material Type
Item Category 1
Status
Searching...
30000010194119 TL152.8 I584 2009 Open Access Book Book
Searching...

On Order

Summary

Summary

In-Vehicle Corpus and Signal Processing for Driver Behavior is comprised of expanded papers from the third biennial DSPinCARS held in Istanbul in June 2007. The goal is to bring together scholars working on the latest techniques, standards, and emerging deployment on this central field of living at the age of wireless communications, smart vehicles, and human-machine-assisted safer and comfortable driving. Topics covered in this book include: improved vehicle safety; safe driver assistance systems; smart vehicles; wireless LAN-based vehicular location information processing; EEG emotion recognition systems; and new methods for predicting driving actions using driving signals.

In-Vehicle Corpus and Signal Processing for Driver Behavior is appropriate for researchers, engineers, and professionals working in signal processing technologies, next generation vehicle design, and networks for mobile platforms.


Table of Contents

Bruce Magladry and Deborah BruceSadayuki TsugawaHuseyin Abut and Hakan Erdogan and Aytul Ercil and Baran Curuklu and Hakki Can Koman and Fatih Tas and Ali Ozgur Argunsah and Serhan Cosar and Batu Akan and Harun Karabalkan and Emrecan Cokelek and Rahmi Ficici and Volkan Sezer and Serhan Danis and Mehmet Karaca and Mehmet Abbak and Mustafa Gokhan Uzunbas and Kayhan Eritmen and Mumin Imamoglu and Cagatay KarabatChiyomi Miyajima and Takashi Kusakawa and Takanori Nishino and Norihide Kitaoka and Katsunobu Itou and Kazuya TakedaPongtep Angkititrakul and John H.L. Hansen and Sangjo Choi and Tyler Creek and Jeremy Hayes and Jeonghee Kim and Donggu Kwak and Levi T. Noecker and Anhphuc PhanSeigo Ito and Nobuo KawaguchiEnrico Masala and Juan Carlos De MartinEsra Vural and Mujdat Cetin and Aytul Ercil and Gwen Littlewort and Marian Bartlett and Javier MovellanKenji Mase and Koji Imaeda and Nobuyuki Shiraki and Akihiro WatanabeMa Li and Quek Chai and Teo Kaixiang and Abdul Wahab and Huseyin AbutMarco Moebus and Abdelhak ZoubirNaoki Shibata and Goro ObinataUlku Cagri Akargun and Engin ErzinLuis Buera and Antonio Miguel and Eduardo Lleida and Alfonso Ortega and Oscar SazYasunari Obuchi and Nobuo HataokaToshihiko Itoh and Shinya Yamada and Kazumasa Yamamoto and Kenji ArakiKihyeon Kim and Changwon Jeon and Junho Park and Seokyeong Jeong and David K. Han and Hanseok KoBowon Lee and Mark Hasegawa-JohnsonWooil Kim and John H. L. Hansen
1 Improved Vehicle Safety and How Technology Will Get Us There, Hopefullyp. 1
2 New Concepts on Safe Driver-Assistance Systemsp. 9
3 Real-World Data Collection with "UYANIK"p. 23
4 On-Going Data Collection of Driving Behavior Signalsp. 45
5 UTDrive: The Smart Vehicle Projectp. 55
6 Wireless Lan-Based Vehicular Location Information Processingp. 69
7 Perceptually Optimized Packet Scheduling for Robust Real-Time Intervehicle Video Communicationsp. 83
8 Machine Learning Systems for Detecting Driver Drowsinessp. 97
9 Extraction of Pedestrian Regions Using Histogram and Locally Estimated Feature Distributionp. 111
10 EEG Emotion Recognition Systemp. 125
11 Three-Dimensional Ultrasound Imaging in Air for Parking and Pedestrian Protectionp. 137
12 A New Method for Evaluating Mental Work Load In n-Back Tasksp. 149
13 Estimation of Acoustic Microphone Vocal Tract Parameters from Throat Microphone Recordingsp. 161
14 Cross-Probability Model Based on Gmm for Feature Vector Normalizationp. 171
15 Robust Feature Combination for Speech Recognition Using Linear Microphone Array in a Carp. 187
16 Prediction of Driving Actions from Driving Signalsp. 197
17 Design of Audio-Visual Interface for Aiding Driver's Voice Commands in Automotive Environmentp. 211
18 Estimation of High-Variance Vehicular Noisep. 221
19 Feature Compensation Employing Model Combination for Robust In-Vehicle Speech Recognitionp. 233
Indexp. 245
Go to:Top of Page