Map Updating System Using Minimum Safety Boundary
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Solution Overview
Problem
Current automated driving systems face challenges in updating high-definition maps in real-time, leading to safety risks due to mismatched road information, requiring efficient and accurate map updating methods to ensure precise vehicle navigation and environment awareness.
Innovation Solution
A method and apparatus for updating maps by calculating a minimum safety boundary based on sensing data from in-vehicle sensors and road surveillance cameras, using pre-trained neural networks to determine the vehicle drivable area and fuse multiple sensor data estimates, allowing for rapid and accurate map updates triggered by abnormal scenarios such as traffic accidents or road construction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional map updating methods are used, then map accuracy can be maintained, but real-time performance deteriorates and safety risks increase
Solution Approach 1:
The system performs preliminary actions by pre-defining abnormal scenario types (accidents, construction, road closures) and pre-establishing the minimum safety boundary calculation framework. When an abnormal scenario is detected, the system immediately activates the pre-prepared update mechanism, avoiding delays in map updating while maintaining accuracy.
Solution Approach 2:
The map updating system performs self-service by automatically detecting abnormal scenarios through sensor data, calculating the minimum safety boundary independently, and executing map updates without requiring external intervention or dedicated collection vehicles. This self-service capability enables real-time updates while reducing time loss.
2Measurement precision
If comprehensive sensor data is processed, then map accuracy improves, but computing power requirements increase
Solution Approach 1:
The system extracts only the essential information needed for map updating from comprehensive sensor data. By focusing on abnormal scenario detection and minimum safety boundary calculation rather than processing all sensor data uniformly, the system maintains high map updating accuracy while significantly reducing computing power requirements.
Solution Approach 2:
The system applies local quality by processing sensor data selectively - intensively analyzing only the regions and parameters relevant to abnormal scenarios and minimum safety boundaries, rather than uniformly processing all sensor information. This localized processing approach maintains accuracy where needed while reducing overall computational energy consumption.
3Adaptability or versatility
If dedicated collection vehicles are used, then map updating coverage is improved, but system complexity and cost increase
Solution Approach 1:
The system achieves universality by enabling any vehicle equipped with standard sensors (cameras, radar, GPS) to participate in map updating, eliminating the need for dedicated collection vehicles. This multi-functional approach allows regular vehicles to perform map updating tasks, improving coverage while reducing system complexity and cost.
Solution Approach 2:
The system eliminates dedicated collection vehicles by enabling self-service map updating where any vehicle can independently detect abnormal scenarios, calculate safety boundaries, and trigger map updates. This self-service mechanism broadens update coverage without adding system complexity or requiring specialized infrastructure.
4Reliability
If real-time map updating is implemented, then driving safety improves, but computing resources and costs increase
Solution Approach 1:
The system extracts only the critical computing tasks necessary for safety - abnormal scenario detection, minimum safety boundary calculation, and targeted map updates. By removing unnecessary computational overhead and focusing resources only on safety-critical operations, the system achieves real-time updating while controlling computing power requirements and costs.
Data Source
AI summary
A map updating method and apparatus (800), and a device (900) are disclosed, which can be used in automated driving (Automated driving), intelligent driving (Intelligent Driving), and other fields. The map updating method includes: when an abnormal scenario occurs, obtaining various types of sensing data of the abnormal scenario, calculating a minimum safety boundary based on the sensing data of the abnormal scenario; and updating a map based on the minimum safety boundary obtained through calculation. According to the map updating method, a map updating program can be triggered when the abnormal scenario occurs, without the need to wait for a collection vehicle/crowd-sourcing vehicle. This improves real-time performance of map refreshing, and ensures safety of an automated driving environment.