By Rui Sun
This thesis introduces a brand new built-in set of rules for the detection of lane-level abnormal riding. to this point, there was little or no development within the skill to become aware of lane point abnormal using types, as a rule because of a scarcity of excessive functionality positioning ideas and appropriate riding trend acceptance algorithms. The set of rules combines information from the worldwide Positioning method (GPS), Inertial size Unit (IMU) and lane info utilizing complicated filtering tools. The motor vehicle country inside of a lane is envisioned utilizing a Particle clear out (PF) and a longer Kalman filter out (EKF). The nation details is then used inside of a unique Fuzzy Inference process (FIS) dependent set of rules to observe kinds of abnormal using. Simulation and box trial effects are used to illustrate the accuracy and reliability of the proposed abnormal using detection method.
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Additional info for An Integrated Solution Based Irregular Driving Detection
The side-on impact related to lane departure is one of the main reasons for motorway and ‘A’ road trafﬁc accidents. Especially, side-on impact accidents at a speed of 60 mph or more, have an high injury rate. Thus, early detection of a vehicle’s irregular behaviour within the lane should prevent lane departure and related accidents (DfT 2013). 1 and Fig. 1 show the reasons for irregular driving. Among all the reasons, the inattention/distraction accounts for 60 % of the accidents, and this category represented the highest percentage of fatalities in the UK in 2012 (DfT 2013).
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. They claimed that, since sleep onset is the most critical consequence of fatigued driving, it is appropriate to separate the issue of sleep onset from the wider analysis of the physiological state of fatigue simply by judging sleep onset based on the eyes opening and closing.
Liu, L. , & Chen, C. M. (2006). A novel driving pattern recognition and status monitoring system. -W. -N. ), Advances in image and video technology, ser. Lecture notes in computer science (Vol. 4319, pp. 504–512). Berlin: Springer. NHTSA. (2010). , & Shirmohammadi, S. (2011). Intelligent driver drowsiness detection through fusion of yawning and eye closure. IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS), 2011, 1–6. , & Wahde, M.
An Integrated Solution Based Irregular Driving Detection by Rui Sun