Vehicle Data Processing with Identification Codes for Trusted Map Updates
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Solution Overview
Problem
Conventional high-precision map updates in autonomous driving are costly and lack security and reliability due to indiscriminate data collection from various vehicles, which are not differentiated in terms of their capabilities or trustworthiness.
Innovation Solution
A data processing method involving the allocation of identification codes based on vehicle type indication information to determine specific processing manners for perception data, enhancing security and reliability by distinguishing between different types of vehicles and their data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If perception data from all crowdsourcing vehicles is collected and processed without distinction, then data volume and map update coverage are improved, but data security and reliability deteriorate
Solution Approach 1:
The patent segments crowdsourcing vehicles into different categories (first vehicles and second vehicles) based on their characteristics, and applies different processing manners to each segment. This segmentation allows the system to maintain comprehensive data collection while ensuring security and reliability by treating different data sources differently according to their trustworthiness and capability levels.
2Manufacturing precision
If conventional high-precision map production methods are used, then initial map production quality is improved, but update costs and complexity increase
Solution Approach 1:
The patent changes the parameter of vehicle classification and assigns different processing manners based on vehicle types. This parameter change enables the system to maintain high map update precision while reducing operational complexity by automating the differentiation process and assigning appropriate processing levels to different vehicle categories, rather than treating all vehicles uniformly.
3Ease of operation
If all vehicles are treated equally in data collection, then ease of operation is improved, but data processing accuracy deteriorates
Solution Approach 1:
The patent introduces dynamic classification of vehicles into different types with corresponding processing manners. This dynamic approach maintains operational simplicity for data collection while improving processing accuracy by adapting the processing method to the specific characteristics and reliability levels of different vehicle types, rather than using a static one-size-fits-all approach.
Data Source
AI summary
A map cloud allocates an identification code to a vehicle, maintains a correspondence between the identification code and a data processing manner, and sends the identification code to the vehicle. When reporting data, the vehicle reports the identification code allocated to the vehicle, that is, identifies the corresponding processing manner of the data by using the identification code, so that after receiving a data report message reported by the vehicle, the map cloud can quickly determine the data processing manner by using the identification code included in the data report message.