Driver State Estimation Based on Dynamic Bayesian Networks Considering Different Age and Gender Groups-Reference-Cited by-同舟云学术

Driver State Estimation Based on Dynamic Bayesian Networks Considering Different Age and Gender Groups

Published:2017-09-24 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct
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Author:

Yang Ji Hyun¹,Jeong Hyeon-Bin¹,Han Jihyuck¹,Lim Sejoon¹

Affiliation:

1. Kookmin University, Seoul, ROK

Funder

Ministry of Science, ICT & Future Planning

Ministry of Science, ICT, and Future Planning

Ministry of Trade, Industry, and Energy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3131726.3131752

Reference9 articles.

1. Yanchao Dong Zhencheng Hu Keiichi Uchimura and Nobuki Murayama. 2011. Driver inattention monitoring system for intelligent vehicles: A review. IEEE transactions on intelligent transportation systems 12 2 (June 2011) 596--614. 10.1109/TITS.2010.2092770 Yanchao Dong Zhencheng Hu Keiichi Uchimura and Nobuki Murayama. 2011. Driver inattention monitoring system for intelligent vehicles: A review. IEEE transactions on intelligent transportation systems 12 2 (June 2011) 596--614. 10.1109/TITS.2010.2092770

2. Korea Road Traffic Authority (KoROAD). 2015. Statistics Analysis of Traffic Accidents in 2015 Rep. no. 2015--0213-025. Korea Road Traffic Authority (KoROAD). 2015. Statistics Analysis of Traffic Accidents in 2015 Rep. no. 2015--0213-025.

3. A dynamic content summarization system for opportunistic driver infotainment

4. Development of driver-state estimation algorithm based on Hybrid Bayesian Network

5. Classifying driver workload using physiological and driving performance data

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