Driving Environment Data Updates Near Nodes for Autonomous Routing
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Solution Overview
Problem
Existing technologies for autonomous driving increase data capacity and reduce reliability of driving environmental data by deleting data points with fewer matches, leading to inefficiencies in data provision during autonomous driving modes.
Innovation Solution
A processing device and method that sets a trigger position ahead of a node on a travel link, updating local driving environmental data with probe information recognized by the vehicle, concentrating updates on areas requiring high accuracy, thereby reducing data capacity while maintaining reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data points with fewer matches are deleted to reduce data capacity, then data capacity is reduced, but reliability of driving environmental data deteriorates
Solution Approach 1:
The patent segments the driving environmental data into different regions based on node proximity. Data near nodes (within threshold distance) is retained with high precision, while data far from nodes is deleted. This segmentation allows selective retention of critical data points that maintain reliability while reducing overall data capacity.
Solution Approach 2:
The patent applies different data retention strategies to different spatial regions. High-quality data retention is applied locally around nodes where accuracy is critical for autonomous driving decisions, while data quality requirements are relaxed in regions farther from nodes. This local quality differentiation maintains reliability where needed while reducing overall data capacity.
2Reliability
If all data points are retained to maintain reliability, then reliability is maintained, but data capacity increases leading to inefficiencies
Solution Approach 1:
The patent extracts and removes redundant data points that are far from nodes and contribute minimally to driving decisions. By identifying and removing these unnecessary data points while preserving critical data near nodes, the system reduces data capacity without significantly compromising reliability.
Solution Approach 2:
The patent performs preliminary filtering of data points based on their distance to nodes before processing. Data points exceeding the threshold distance are pre-identified for deletion, allowing efficient reduction of data capacity before the autonomous driving system processes the remaining critical data.
3Measurement precision
If data is updated continuously throughout the entire link, then data accuracy is improved, but processing time and energy consumption increase
Solution Approach 1:
The patent segments the link into multiple regions based on distance to nodes. Only the region within threshold distance of each node is selected for data updates. This segmentation reduces the total area requiring updates while maintaining accuracy in critical regions near nodes where autonomous driving decisions are most sensitive.
Solution Approach 2:
The patent applies high-quality data updates locally around nodes where precision is critical, while skipping updates in regions farther from nodes. This local quality approach ensures data accuracy where it matters most for driving safety while reducing overall processing time and energy consumption.
Data Source
AI summary
A processing device performing driving environmental data related processing related to a local driving environmental data provided with data in an autonomous driving mode of a host vehicle is provided. The processing device includes a processor and a storage medium. The processor is configured to read the local driving environmental data stored in the storage medium, which includes position information of a node and a link; set a trigger position ahead of the node on the link where the host vehicle is scheduled to travel; and update the local driving environmental data stored in the storage medium with probe information recognized by sensing in the host vehicle, starting from the trigger position.