Geely announces vehicle blind spot detection patents that can reduce the probability of collision accidents – Passionategeekz –

[CNMO Technology News]On June 17, according to Passionategeekz, the patent “A vehicle blind spot detection method, device, electronic equipment and storage medium” applied for by Zhejiang Geely Holding Group Co., Ltd. and Geely Automobile Research Institute (Ningbo) Co., Ltd. was announced.

According to reports, the invention involves the field of intelligent driving. The method provided by this application can accurately determine the target monitoring equipment related to the vehicle when it is in a target scenario where the blind spot is prone to appear in a blind spot in the field of view. This allows real-time video data of blind spots in various directions to be obtained, and the monitoring range is fully expanded to the surroundings of the vehicle, rather than being limited to both sides of the vehicle.

By capturing blind spot videos of various directions in real time, the moving targets are effectively detected and the degree of collision risk between the moving targets and vehicles is analyzed. Based on the analysis results, control operations are performed according to the corresponding risk level, effectively monitoring and response to the vehicle’s all-round blind spots is achieved, making up for the limited monitoring range of existing auxiliary equipment, improving the vehicle’s driving safety, and effectively reducing the probability of collision accidents caused by blind spots in the field of vision.

According to Passionategeekz, the patent “a method, device, electronic equipment, media and products for self-driving driving” applied by Zhejiang Geely Holding Group Co., Ltd. and Geely Automobile Research Institute (Ningbo) Co., Ltd. was authorized.

According to the application example, the method includes obtaining the perceived information of the vehicle during autonomous driving; inputting the perceived information into the escape decision model to obtain the escape strategy output by the escape decision model; implementing the escape strategy to control the vehicle to escape from the trapped scene.

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