Vehicle Guidance via Server-Side Object Model Segmentation
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Solution Overview
Problem
Highly accurate maps required for autonomous vehicle navigation require significant data transmission, leading to high memory and bandwidth demands, making industrialization difficult and accuracy insufficient for reliable semi-autonomous functions.
Innovation Solution
Transmitting only a model of real objects with their position via a communication network, rather than detailed object descriptions, allowing for efficient data transmission and reducing memory requirements, enabling semi-autonomous vehicle guidance by ascertaining object parameters from a database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed object data is transmitted to the vehicle, then the accuracy of driving-environment information is improved, but the data transmission bandwidth and memory requirements increase significantly
Solution Approach 1:
The object data is segmented into two parts: object parameters (transmitted to the vehicle) and object model (stored in the server). This segmentation allows the vehicle to receive only essential identification data while the server retains the detailed model, reducing transmission volume while preserving accuracy for semi-autonomous guidance.
Solution Approach 2:
The server acts as an intermediary between the data source and the vehicle. It stores detailed object models and generates object parameters by matching detected objects with stored models, then transmits only the generated parameters to the vehicle. This intermediary processing reduces the data burden on the vehicle while maintaining information accuracy.
2Reliability
If comprehensive object parameters are transmitted, then the reliability of semi-autonomous guidance is improved, but the complexity of the communication system increases
Solution Approach 1:
The server performs preliminary actions by pre-storing detailed object models and pre-generating object parameters through model matching before the vehicle needs them. This preliminary processing ensures reliable guidance data is ready in advance, reducing the complexity of real-time communication and processing at the vehicle end.
Solution Approach 2:
Instead of transmitting the complete original object model to the vehicle, the server creates a simplified copy (object parameters) by matching detected objects with stored models. This copying process extracts only the essential information needed for reliable semi-autonomous guidance, reducing communication complexity while maintaining data reliability.
3Speed
If real-time object data is transmitted, then the responsiveness of vehicle guidance is improved, but the bandwidth consumption increases
Solution Approach 1:
The system extracts only the essential object parameters from the complete object model for transmission to the vehicle. By taking out only the necessary identification and guidance-relevant parameters while leaving the detailed model in the server, the system achieves responsive real-time guidance with minimal bandwidth consumption.
Solution Approach 2:
The server transmits partial object information (object parameters) rather than the complete object model. This partial action provides sufficient data for responsive semi-autonomous guidance while avoiding the excessive bandwidth consumption that would result from transmitting full real-time object data.
Data Source
AI summary
A method for operating a vehicle, including receiving a model of a real object and a position of the object via a communication network, ascertaining one or more object parameters based on the model received, and at least semi-autonomous guidance of the vehicle based on the one or more object parameters and the position. A corresponding apparatus, a method and an apparatus for providing driving-environment information, as well as a vehicle and a computer program, as also described.