Huawei’s New Sensor Patent Helps Improve Driving Safety
The automatic driving system is generally composed of cameras, radars and processors, and realizes automatic driving functions through the combination of software and hardware. However, the camera has a certain blind area of vision, and the detection range of the radar is also limited, so it is difficult to accurately detect all the surrounding obstacles.
Recently, Huawei published a new patent on autonomous driving technology, which integrates the distribution information of obstacles they perceive by fusing two types of sensors: camera and radar. After fusion, the areas the vehicle can pass through will be presented from the system in probabilistic form. Huawei’s new patent can make the vehicle more comprehensively perceive obstacles around the vehicle, thereby effectively improving the safety of autonomous driving technology.
Huawei’s patent principle can be roughly divided into two steps. The first step is that the system obtains the first probability distribution of obstacles through the camera, and the second step is to obtain the second probability distribution of obstacles according to the echo time and echo width of the radar echo signal. According to the joint analysis of the first probability distribution and the second probability distribution of the obstacles, the system obtains the drivable area of the vehicle represented by the probability, and the probability represents the probability that the vehicle is impassable.
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The data includes multiple ages, multiple time periods and multiple races (Caucasian, Black, Indian). The driver behaviors includes dangerous behavior, fatigue behavior and visual movement behavior.
The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). The passenger behaviors include passenger normal behavior, passenger abnormal behavior(carsick behavior, sleepy behavior, lost items behavior).
The data includes male and female. The age distribution ranges from child to the elderly. For each person, 7 images were collected. The data diversity includes different facial postures, different expressions, different light conditions and different scenes.
The data is collected from 1,078 Chinese people. Each subject was collected once a week, 6 times in total, so the time span is 6 weeks.
This data diversity includes multiple scenes, 18 gestures, 5 shooting angels, multiple ages and multiple light conditions. For annotation, gesture 21 landmarks (each landmark includes the attribute of visible and visible), gesture type and gesture attributes were annotated.
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