Digital Map Superelevation Mining for Vehicle Safety
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Solution Overview
Problem
Current advanced driver assistance systems (ADAS) face challenges in consistently identifying and providing warnings for locations on the road network where traffic accidents frequently occur, as accident data is scattered across different administrative entities and formats, making it difficult to obtain reliable information on hazardous conditions such as curves with insufficient superelevation.
Innovation Solution
A system that uses a database representing the road network to identify curves with negative superelevation, adding precautionary action data to alert drivers or modify vehicle operations when approaching such locations, combining with positioning systems and other sensors for enhanced safety measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital map data is used to identify hazardous road conditions, then driver safety is improved, but system complexity increases due to data mining requirements
Solution Approach 1:
The system performs preliminary data mining and analysis of digital map data to identify hazardous road conditions (such as curves with insufficient superelevation) before the vehicle reaches them. This advance identification allows the system to prepare warnings and recommendations in advance, improving safety response time while managing complexity through pre-processing rather than real-time analysis.
Solution Approach 2:
The system introduces an intermediary processing layer that acts between the raw digital map data and the driver alert system. This intermediary layer mines and analyzes the map data to extract hazardous conditions, transforming complex raw data into simplified hazard identifiers that can be easily communicated to drivers, thus bridging the gap between data complexity and user comprehension.
2Measurement precision
If comprehensive digital map data analysis is performed to identify all hazardous conditions, then measurement precision of road conditions is improved, but loss of time in data processing increases
Solution Approach 1:
The system extracts and focuses specifically on critical hazardous road conditions (such as curves with insufficient superelevation, steep grades, and sharp bends) from the comprehensive digital map data, rather than analyzing all possible road characteristics. This selective extraction maintains high measurement precision for safety-critical conditions while significantly reducing overall data processing time by ignoring non-essential features.
Solution Approach 2:
The system applies different levels of analysis depth to different road segments based on their hazard potential. High-priority areas such as curves and intersections receive detailed superelevation and geometry analysis, while straight low-risk segments receive minimal processing. This localized quality approach ensures measurement precision is concentrated where it matters most for safety, reducing unnecessary processing time in safe areas.
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 with insufficient superelevation. A database that represents the road network is used to determine locations where curved sections of roads have insufficient superelevation (banking), i.e., where the superelevation is below a threshold. 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 of insufficient superelevation. 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 of insufficient superelevation.