Driving state monitoring methods and apparatuses, driver monitoring systems, and vehicles
AssigneeBeijing Sensetime Technology Development Co., Ltd.
InventorsFei Wang, Chen Qian
Life of this patent
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Abstract
Embodiments of the present application disclose driving state monitoring methods and apparatuses, driver monitoring systems, and vehicles. The driving state monitoring method includes: performing driver state detection on a driver image; and performing at least one of: outputting a driving state monitoring result of a driver or performing intelligent driving control based on a result of the driver state detection. The embodiments of the present application can implement real-time monitoring of the driving state of a driver, so as to take corresponding measures in time when the driving state of the driver is poor, to ensure safe driving and avoid road traffic accidents.
Claims27
A driving state monitoring method, comprising:
- performing driver state detection on a driver image;
- and
- performing at least one of:
- outputting a driving state monitoring result of a driver or performing intelligent driving control based on a result of the driver state detection;
- wherein the driver state detection comprises at least one of:
- driver fatigue state detection, driver distraction state detection, or driver scheduled distraction action detection,
- wherein in response to the scheduled distraction action being the smoking action, the performing driver state detection on a driver image comprises:
- performing face detection on the driver image via a fourth neural network to obtain a face detection frame, and extracting feature information of the face detection frame;
- and
- determining whether the smoking action occurs via the fourth neural network based on the feature information of the face detection frame.
A driving state monitoring apparatus, comprising:
- a processor;
- and
- a memory storing instructions, the instructions when executed by the processor, cause the processor to perform operations, the operations comprising:
- performing driver state detection on a driver image;
- and
- performing at least one of:
- outputting a driving state monitoring result of a driver based on a result of the driver state detection or performing intelligent driving control based on the result of the driver state detection;
- wherein the driver state detection comprises at least one of:
- driver fatigue state detection, driver distraction state detection, or driver scheduled distraction action detection,
- wherein, in response to the scheduled distraction action being the smoking action, the performing driver state detection on a driver image comprises:
- performing face detection on the driver image via a fourth neural network to obtain a face detection frame, and extracting feature information of the face detection frame;
- and
- determining whether the smoking action occurs via the fourth neural network based on the feature information of the face detection frame.
A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor, causes the processor to perform operations, the operations comprising:
- performing driver state detection on a driver image;
- and
- performing at least one of:
- outputting a driving state monitoring result of a driver based on a result of the driver state detection or performing intelligent driving control based on the result of the driver state detection;
- wherein the driver state detection comprises at least one of:
- driver fatigue state detection, driver distraction state detection, or driver scheduled distraction action detection,
- wherein, in response to the scheduled distraction action being the smoking action, the performing driver state detection on a driver image comprises:
- performing face detection on the driver image via a fourth neural network to obtain a face detection frame, and extracting feature information of the face detection frame;
- and
- determining whether the smoking action occurs via the fourth neural network based on the feature information of the face detection frame.
Dependencies are inferred from the claim text.