Data processing method and device, equipment and storage medium

Abstract

The invention discloses a data processing method and device, equipment and a storage medium. The method comprises the steps that first position information and first sensing data of a target vehicle and second position information and second sensing data of a current vehicle are acquired; based on the first position information and the second position information, determining relative position information between the target vehicle and the preceding vehicle and a sensing data detection blind area corresponding to the current vehicle; determining blind area standby sensing data corresponding to the sensing data detection blind area from the first sensing data based on the sensing data detection blind area; according to the relative position information, the second sensing data and the sensing data detection blind area, performing segmentation processing on the blind area standby sensing data to obtain target blind area sensing data corresponding to the sensing data detection blind area; updating blind area sensing data in the second sensing data based on the target blind area sensing data to obtain target sensing data of the current vehicle; according to the invention, the ability of bicycle automatic driving in automatic driving can be improved.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular to a data processing method, device, device and storage medium. Background technique [0002] Autonomous driving related technologies are not only applied to small cars, but also to large long-distance vehicles; different from small vehicles, the automatic driving of large long-distance vehicles not only applies single-vehicle intelligence, but also has more automatic functions in fleet coordination. driving application. [0003] In the scenario where the rear vehicle follows the front vehicle during the automatic driving process, for example, in the long-distance transportation of the truck fleet in the automatic driving fleet: it is generally composed of the leading vehicle and the following vehicle, and the vehicle speed is relatively uniform. The leading car generally has a better front view, and the front-view camera, lidar and millimeter-wave radar can p...

AI Technical Summary

Problems solved by technology

Since the fleet vehicles traveling in formation are generally large in size, for the following vehicles, the leading vehicle and the vehicle in front block the rear vehicles to varying degrees, which will affect the information collection of the front lane, vehicle and road signs by the rear vehicles. It brings risks. The blocked signals include forward-looking video signals, lidar signals, and millimeter-wave radar signals. The blocked information directly affects the bicycle intelligence of the following vehicles, and the congestion, obstacles, and signs in front of the lane cannot be timely and effective. Judgment, braking, lane change, emergency stop and other decisions lack the input of necessary information; it brings great risks to the overall autonomous driving of the fleet

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