By Rui Sun
This thesis introduces a brand new built-in set of rules for the detection of lane-level abnormal using. up to now, there was little or no development within the skill to notice lane point abnormal using types, in most cases because of an absence of excessive functionality positioning innovations and appropriate using trend attractiveness algorithms. The set of rules combines facts from the worldwide Positioning method (GPS), Inertial dimension Unit (IMU) and lane info utilizing complex filtering tools. The car country inside a lane is expected utilizing a Particle filter out (PF) and a longer Kalman clear out (EKF). The kingdom details is then used inside of a unique Fuzzy Inference procedure (FIS) established set of rules to become aware of varieties of abnormal using. Simulation and box trial effects are used to illustrate the accuracy and reliability of the proposed abnormal riding detection method.
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Extra info for An Integrated Solution Based Irregular Driving Detection
Scale factor and nonlinearity errors describe the uncertainty in linear and nonlinear scaling between the input and output, respectively. Random noise exists in the inertial output. Each of these error categories in general includes some or all of the following terms: ﬁxed, turn-on to turn-on variations, random walk, and temperature variations (Grewal et al. 2007). Calibration of the sensor can estimate and compensate for the ﬁxed terms, such as the temperature varying terms.
From the table above, accuracy is an important requirement for the PNT system in many ITS applications. Currently, however, there are no speciﬁc requirements deﬁned for lane level irregular driving detection systems, despite the fact that this forms an important basis for many safety related ITS applications. Based on the general requirements for the PNT system, the accuracy is developed as the requirement for the precise positioning algorithm of the lane level irregular driving detection system.
Zhu and Ji (2004) used two cameras on the dashboard to capture the visual cues of drivers, such as eyelid movement, gaze movement, head movement and facial expression, in order to predict fatigue with a probabilistic model. Lee et al. (2006) used cameras to capture the driver’s sight line and driving path and calculated the correlation between them in order to monitor the driving status and patterns. Albu et al. (2008) also used a vision-based system to monitor eye conditions in order to detect fatigue while driving.
An Integrated Solution Based Irregular Driving Detection by Rui Sun