Digital Map Curvature Mining for Vehicle Precautionary Actions
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Solution Overview
Problem
Current advanced driver assistance systems (ADAS) face challenges in consistently identifying and providing warnings for potentially hazardous driving conditions, such as curves with decreasing radii of curvature, due to disparate accident statistics and limited predictive capabilities beyond sensor ranges.
Innovation Solution
A system that utilizes a database representing the road network to identify locations with significant changes in curvature, adding precautionary action data to alert drivers or modify vehicle operations, such as providing warnings, adjusting control systems, or activating sensors, when approaching curves with decreasing radii of curvature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital map data is used to provide predictive capability beyond sensor range, then the ability to determine road conditions around corners and over hills is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system pre-processes digital map data to identify and store curvature characteristics, hazard locations, and road geometry information before they are needed for decision-making. This preliminary action allows the system to quickly retrieve pre-analyzed data during vehicle operation, reducing real-time processing complexity while maintaining reliable predictive capability.
Solution Approach 2:
The patent introduces an intermediary data processing layer that acts as a mediator between the raw digital map data and the vehicle control systems. This intermediary layer pre-analyzes map data to extract relevant features (curvature, hazards, geometry) and presents them in a simplified format to the control systems, reducing the complexity burden on the overall system while maintaining reliable predictive capabilities.
2Measurement precision
If data mining is performed on digital map database to identify decreasing radius of curvature, then hazard detection accuracy is improved, but the data processing time and computational load increase
Solution Approach 1:
The system performs data mining and identifies decreasing radius of curvature locations in advance during map data loading or off-line processing. These pre-identified hazard locations are stored in the digital map database, allowing the vehicle system to quickly retrieve and use this information without performing complex real-time data mining, thus maintaining high detection accuracy while minimizing processing time during vehicle operation.
Solution Approach 2:
The patent segments the road network into discrete road sections with specific curvature characteristics. By dividing the continuous road geometry into segmented sections with defined curvature properties, the system can efficiently query and analyze specific segments for decreasing radius conditions without processing the entire road network in real-time, thereby improving detection accuracy for hazardous sections while reducing overall processing time.
3Reliability
If precautionary action data is added to digital map database, then driver safety warning capability is improved, but the database complexity and storage requirements increase
Solution Approach 1:
The system adds precautionary action data with specific local quality characteristics to relevant road sections in the digital map database. Instead of uniformly adding complex data structures throughout the entire database, the patent applies detailed precautionary information (such as warning types, recommended actions, and hazard specifics) only to locations where decreasing radius of curvature and other hazards are identified, maintaining database efficiency while improving safety warning capability where needed.
Data Source
AI summary
Disclosed is a feature for a vehicle that enables taking precautionary actions in response to conditions on the road network around or ahead of the vehicle, in particular, a curved portion of a road where the curvature increases between adjacent curved sections. A database that represents the road network is used to determine locations where curvature between adjacent curved sections increases. Then, precautionary action data is added to the database to indicate a location at which a precautionary action is to be taken about the location where curvature increases. A precautionary action system installed in a vehicle uses this database, or a database derived therefrom, in combination with a positioning system to determine when the vehicle is at a location that corresponds to the location of a precautionary action. When the vehicle is at such a location, a precautionary action is taken by a vehicle system as the vehicle is approaching a location where the curvature increases.