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.
"The advances and improvements in sensing and computation technologies led to the emergence of driving monitoring system which fall into two major categories; monitoring of drivers vigilance systems and vehicle-human interactions systems. Driver's vigilance systems are used to measure the driver alertness by using visual observation of drivers   or by monitoring driver's physiological signals . On the other hand, vehicle-human interactions systems are used to identify and quantify the relationships between driver's behavioural indices and traffic safety. "
[Show abstract][Hide abstract] ABSTRACT: Driver behaviour recognition or driving style classification systems have become very important for reducing vehicle accidents and providing safety. Human driver behavior recognition systems are used to estimate human behaviour by observing driver interaction with the vehicle and environment. These systems goal is to infer the driver's behaviour by mapping some parameters that represent the driver interaction with control elements available in the vehicle, such as speed, accelerator, following distance, and etc. The driving style recognition approach developed in this paper is based on the theory of fuzzy logic, which provides a methodology for reasoning about the application area that approximates human reasoning. One key aspect of the method developed in this paper is the great reduction of the rules of the rule-base inference engine by using a decomposed fuzzy system. The method is based on introducing weighting factors for the sensor inputs, thus inferring the reflexive conclusions from each input to the system rather than putting all the possible states of all the inputs to infer a single conclusion. The effectiveness and reliability of the proposed method is evaluated and compared with data obtained from an expert judgment for a 25 driving styles.
"For example, Zhu et al.  used cameras to capture eyelid movement, head movement , and facial expression to predict driver fatigue based on probabilistic model. Lee et al.  used cameras to capture the driver's sight line and the driving path and calculated the correlation between them to monitor the driving status and patterns. Also, abnormal driving can be detected using physiological signals. "
[Show abstract][Hide abstract] ABSTRACT: Abstract—Safer driver behavior promoting is the main goal of
this paper. It is a fact that drivers behavior is relatively safer when
being monitored. Thus, in this paper, we propose a monitoring
system to report speciﬁc driving event as well as the potentially
aggressive events for estimation of the driving performance. Our
driving monitoring system is composed of two parts. The ﬁrst part
is the in-vehicle embedded system which is composed of a GPS
receiver, a two-axis accelerometer, radar sensor, OBD interface, and
GPRS modem. The design considerations that led to this architecture
is described in this paper. The second part is a web server where an
adaptive hierarchical fuzzy system is proposed to classify the driving
performance based on the data that is sent by the in-vehicle embedded
system and the data that is provided by the geographical information
system (GIS). Our system is robust, inexpensive and small enough
to ﬁt inside a vehicle without distracting the driver.
"For example, Zhu et al.  used cameras to capture eyelid movement, head movement, and facial expression to predict driver fatigue based on probabilistic model. Lee et al.  used cameras to capture the driver's sight line and the driving path and calculated the correlation between them to monitor the driving status and patterns. Also, abnormal driving can be * This work was not supported by any organization A. Aljaafreh is with Electrical Engineering Department, Tafila Technical University, Tafila, 66110, Jordan firstname.lastname@example.org "
[Show abstract][Hide abstract] ABSTRACT: Detection and classification of aggressive driving can be based on the use of physiological signals or biometric information like electrocardiogram, electro dermal activity, and respiration. This research proposes a driving performance inference system based on the signature of acceleration in the two dimensions and speed. Driving style can be categorized to: below normal, normal, aggressive, and very aggressive. One of the targets of this paper is to recognize the driving events that fall into each of these categories. Many of driving styles are a major cause of traffic crashes. Many of these styles can be detected with reasonable accuracy using driving performance. The main idea is to utilize the 2-axis accelerometer that is embedded in most of the GPS tracker devices that are used in vehicle tracking and fleet management to recognize the driving styles. This method can be utilized for vehicle active safety purpose. Fuzzy logic inference system is used in classification of the extracted feature to the predefined driving styles. the power of the Euclidean norm of longitudinal and lateral is used as input to the fuzzy inference system in addition to the speed. Classification of the driving style is real time and almost cost nothing.
Vehicular Electronics and Safety (ICVES), 2012 IEEE International Conference on; 01/2012
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