Autonomous Driving QoS Map Updates via Predictive Buffering
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Solution Overview
Problem
Autonomous driving in vehicles is hindered by the unreliability of static map data and inconsistent mobile network coverage, leading to frequent switches from autonomous to manual driving modes due to inadequate and unpredictable Quality of Service (QoS) in wireless networks.
Innovation Solution
A driver assistance system utilizing a backend-server that maintains up-to-date QoS maps for mobile network operators, schedules map data downloads based on vehicle positioning data, and includes a control module to manage autonomous driving, ensuring continuous operation by anticipating coverage gaps and improving network performance through machine learning and communication with mobile network operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If map data is downloaded via mobile communication network on demand, then the vehicle can obtain up-to-date map data, but the download reliability is poor due to incomplete network coverage and variable QoS
Solution Approach 1:
The system performs preliminary actions by predicting future network coverage conditions along the vehicle's route and pre-downloading map data to buffer storage before the vehicle enters areas with poor or no network coverage. This ensures map data is available even when network conditions deteriorate, resolving the contradiction between needing up-to-date data and ensuring download reliability.
Solution Approach 2:
The system dynamically adjusts the map data download strategy based on real-time network QoS conditions, vehicle position, and predicted route characteristics. When network conditions are good, it downloads more data; when conditions deteriorate, it switches to using pre-buffered data. This dynamic adaptation resolves the contradiction by making the system responsive to changing network reliability.
2Ease of operation
If the vehicle switches to manual driving mode when map data download fails, then the vehicle can operate without autonomous mode limitations, but the availability of autonomous driving is reduced
Solution Approach 1:
The system pre-loads map data into buffer storage before the vehicle enters regions with poor network coverage or before autonomous mode transitions might be needed. This preliminary action ensures that autonomous driving can continue uninterrupted even if network conditions prevent real-time data downloads, thereby maintaining autonomous mode availability without sacrificing operational flexibility.
Solution Approach 2:
The buffer storage acts as an intermediary between the network and the autonomous driving system. It decouples the autonomous driving functionality from real-time network availability, allowing the vehicle to maintain autonomous mode even when network conditions are poor, thus resolving the contradiction between operational flexibility and autonomous mode availability.
3Reliability
If static map data is stored in vehicle memory, then the vehicle can operate autonomously without network dependency, but the map data becomes outdated and unreliable
Solution Approach 1:
The system dynamically manages map data by continuously updating it when network conditions permit, while maintaining autonomous operation capability using the most recent available data. It adapts the update frequency and data refresh strategy based on network QoS conditions, vehicle location, and the age of existing map data, thereby resolving the contradiction between maintaining current map data and ensuring reliable autonomous operation.
Solution Approach 2:
The system performs preliminary updates of map data during periods of good network coverage, storing updated data in buffer storage before network conditions deteriorate. This allows the vehicle to operate with current map data even when later network conditions prevent real-time updates, resolving the contradiction between map data currency and autonomous operation reliability.
4Reliability
If the vehicle continuously monitors network QoS to ensure map data download, then the autonomous mode availability is improved, but the system complexity increases
Solution Approach 1:
The system implements self-service by autonomously monitoring network QoS conditions, predicting coverage gaps, and automatically managing map data downloads and buffer updates without requiring complex external control systems. The vehicle's own sensors and processors are used to make decisions about when and what to download, reducing system complexity while maintaining high autonomous mode availability through intelligent self-management.
Data Source
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AI summary
The present invention relates to a driver assistance system for improving the availability and reliability in vehicles operating in an autonomous driving mode. The driver assistance system comprises at least one backend-server. The backend-server comprises a Quality of Services (QoS) map module operable to maintain at least one up-to-date QoS map for at least one mobile network operator (MNO); and a scheduling module operable to schedule appropriate map data downloads to the vehicle. The driver assistance system further comprises at least one vehicle, the vehicle comprising a transmission module operable to transmit driving data to the backend-server in predefined time intervals, the driving data comprising positioning data of the vehicle and network performance data based on passive and active measurements for the at least one MNO; to receive, from the scheduling module in the backend-server the appropriate map data, wherein the scheduling module schedules the appropriate map data downloads based on the transmitted driving data; and to control the vehicle in the autonomous driving mode based on the appropriate map data.