Huawei Technologies Co. Ltd published a patent for “A Method and Terminal Equipment for Identifying Abnormal Vehicle Parameters in a Vehicle Queue” on April 16, with the publication number CN112673406A.
According to the patent summary of the company, this application provides a method and terminal equipment for identifying abnormal vehicle parameters in a vehicle queue based on networked information. This method is applied in the fields of networked vehicles, smart cars, autonomous driving, and V2X, and can identify smart networks.
Moreover, the number of vehicles in the abnormal area of the connected vehicle fleet and the location and speed information of each vehicle enable the abnormal area of the intelligently networked vehicle fleet to be restored to order and realize the coordinated control of the intelligently networked vehicle fleet.
The method includes:
- Determine the current driving scene of the target vehicle based on the driving trajectory data of the target vehicle and the current real-time map of the target vehicle. The driving scene of the target vehicle is an urban road driving scene or a highway driving scene, and the target vehicle is intelligent Intelligent connected cars of connected fleets.
- Selecting a vehicle trajectory generation algorithm in the current driving scene of the target vehicle according to the current driving scene of the target vehicle.
- Determine at least one abnormal area included in front of the target vehicle at time Tb, the at least one abnormal area is an abnormal area in the intelligently networked fleet where the target vehicle is located, and the Tb time is the current time.
- Based on the selected vehicle trajectory generation algorithm and machine learning algorithm, identify the number of vehicles in each abnormal area in at least one abnormal area at time Ta, and the position information and speed of each vehicle included in each abnormal area Information, the Ta time is the calibration time in the historical driving process of the target vehicle.
- Based on the selected vehicle trajectory generation algorithm, according to the number of vehicles in each abnormal area at the time Ta, the position information and speed information of each vehicle included in each abnormal area, the identification at time Tb. The number of vehicles in each abnormal area, the position information, and speed information of each vehicle included in each abnormal area.
|VIA|
