A Novel Driving Pattern Recognition and Status Monitoring System.
ABSTRACT This paper describes a novel driving pattern recognition and status monitoring system based on the orientation information.
Two fixed cameras are used to capture the driver’s image and the front-road image. The driver’s sight line and the driving
lane path are found from these 2 captured images and are mapped into a global coordinate. Two correlation coefficients among
the driver’s sight line, the driving lane path and the car heading direction are calculated in the global coordinate to monitor
the driving status such as a safe driving status, a risky driving status and a dangerous driving status. The correlation coefficients
between the lane path and car heading direction in a fixed period are analyzed and recognized as one of 4 driving patterns
by HMM. Four driving patterns including the driving in a straight lane, the driving in a curve lane, the driving of changing
lanes, and the driving of making a turn are able to be recognized so far.
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Conference Paper: Mobile phone based drunk driving detection[Show abstract] [Hide abstract]
ABSTRACT: Drunk driving, or officially Driving Under the Influence (DUI) of alcohol, is a major cause of traffic accidents throughout the world. In this paper, we propose a highly efficient system aimed at early detection and alert of dangerous vehicle maneuvers typically related to drunk driving. The entire solution requires only a mobile phone placed in vehicle and with accelerometer and orientation sensor. A program installed on the mobile phone computes accelerations based on sensor readings, and compares them with typical drunk driving patterns extracted from real driving tests. Once any evidence of drunk driving is present, the mobile phone will automatically alert the driver or call the police for help well before accident actually happens. We implement the detection system on Android G1 phone and have it tested with different kinds of driving behaviors. The results show that the system achieves high accuracy and energy efficiency.Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2010 4th International Conference on-NO PERMISSIONS; 04/2010
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ABSTRACT: This paper proposes a method for monitoring driver safety levels using a data fusion approach based on several discrete data types: eye features, bio-signal variation, in-vehicle temperature, and vehicle speed. The driver safety monitoring system was developed in practice in the form of an application for an Android-based smartphone device, where measuring safety-related data requires no extra monetary expenditure or equipment. Moreover, the system provides high resolution and flexibility. The safety monitoring process involves the fusion of attributes gathered from different sensors, including video, electrocardiography, photoplethysmography, temperature, and a three-axis accelerometer, that are assigned as input variables to an inference analysis framework. A Fuzzy Bayesian framework is designed to indicate the driver's capability level and is updated continuously in real-time. The sensory data are transmitted via Bluetooth communication to the smartphone device. A fake incoming call warning service alerts the driver if his or her safety level is suspiciously compromised. Realistic testing of the system demonstrates the practical benefits of multiple features and their fusion in providing a more authentic and effective driver safety monitoring.Sensors 01/2012; 12(12):17536-52. · 2.05 Impact Factor
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ABSTRACT: This paper presents a methodological approach to traffic condition recognition, based on driving segment clustering. Traffic condition recognition has many applications to various areas, such as intelligent transportation, adaptive cruise control, pollutant emissions dispersion, safety, and intelligent control strategies in hybrid electric vehicles. This study focuses on the application of driving condition recognition to the intelligent control of hybrid electric vehicles. For this purpose, driving features are identified and used for driving segment clustering, using the kk-means clustering algorithm. Many combinations of driving features and different numbers of clusters are evaluated, in order to achieve the best traffic condition recognition results. The results demonstrate that traffic conditions can be correctly recognized in 87 percent of situations using the proposed approach.Scientia Iranica 08/2011; 18(4):930–937. · 0.54 Impact Factor