Real-time driving danger-level prediction

Authors:
Jinjun Wang;Wei Xu;Yihong Gong
Affiliations:
NEC Laboratories America, Inc., 10080 North Wolfe Road, Cupertino, CA 95014, USA;NEC Laboratories America, Inc., 10080 North Wolfe Road, Cupertino, CA 95014, USA;NEC Laboratories America, Inc., 10080 North Wolfe Road, Cupertino, CA 95014, USA
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

Citing 6
Cited 3

Real-time eye, gaze, and face pose tracking for monitoring driver vigilance

Real-Time Imaging
Rule-based anomaly pattern detection for detecting disease outbreaks

Eighteenth national conference on Artificial intelligence
An Algorithm for Real-Time Stereo Vision Implementation of Head Pose and Gaze Direction Measurement

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Normalizing multi-subject variation for drivers' emotion recognition

ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Detecting stress during real-world driving tasks using physiological sensors

IEEE Transactions on Intelligent Transportation Systems
Real-time system for monitoring driver vigilance

IEEE Transactions on Intelligent Transportation Systems

Multivariate analysis of human behavior data using fuzzy windowing: Example with driver-car-environment system

Engineering Applications of Artificial Intelligence
Genetic optimization of a vehicle fuzzy decision system for intersections

Expert Systems with Applications: An International Journal
Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a driving danger-level prediction system that uses multiple sensor inputs and statistical modeling to predict the driving risk. Three types of features were collected for the research, specifically the vehicle dynamic parameter, the driver's physiological data and the driver's behavior feature. To model the temporal patterns that lead to safe/dangerous driving state, several sequential supervised learning algorithms were evaluated in the paper, including hidden Markov model, conditional random field and reinforcement learning. Experimental results showed that using reinforcement learning based method with the vehicle dynamic parameters feature outperforms the rest algorithms, and adding the other two features could further improve the prediction accuracy. Based on the result, a live driving danger-level prediction prototype system was developed. Compared to many previous researches that focused on monitoring the driver's vigilance level to infer the possibility of potential driving risk, our live system is non-intrusive to the driver, and hence it is very desirable for driving danger prevention applications. Subjective on-line user study of our prototype system gave promising results.