Obstacle detection is one of the important works in the field of driverless vehicle. Camera, millimeter wave radar, LIDAR and other sensors are widely used in the obstacle detection of driverless vehicle. However, cameras and millimeter wave radar are highly dependent on the environment and greatly affected by external factors, especially in the process of high-speed driving, which is easy to produce serious system errors, and even unable to detect obstacles, which has a great potential for the safety of driverless vehicle. LIDAR is used to detect obstacles by launching laser, which is not easy to be interfered by the environment. It has high precision and is suitable for outdoor environment. The main contributions of this paper are as follows: 1) a 16 line LIDAR track recognition method based on driverless equation is proposed. 2) it is easy to get the obstacle coordinates through plane segmentation and clustering for the use of subsequent camera and LIDAR fusion.
Aiming at the problem of fatigue driving, this paper proposed a driver fatigue tracking and detection method combined with OpenMV. OpenMV is used for image acquisition, and the Dlib feature point model is used to locate the detected driver's face. The aspect ratio of eyes is calculated to judge the opening and closing of eyes, and then fatigue detection is performed by PERCLOS (Percentage of Eyelid Closure over the Pupil). The cradle head system is mounted for driver fatigue tracking and detection. The results show that the dynamic tracking system can improve the accuracy to 92.10% for relaxation and 85.20% for fatigue of driver fatigue detection. This system can be applied to the monitoring of fatigue driving very effectively.
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