Local High-Definition Map Assembly from V2X Path Histories
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The lack of high-definition maps in many regions hinders the precision of map-matching and decision-making in V2X applications, especially in developed countries, where creating HD maps is costly and time-consuming, and existing methods fail to account for real-time traffic data or temporary disruptions.
Innovation Solution
A method for assembling a local high-definition map using V2X communication to collect path histories from nearby entities, constructing an undirected graph, and reducing redundancies to generate a precise lane-level map that reflects real-time road conditions, including temporary disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HD maps are created using traditional mapping methods, then map detail and precision are improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent creates virtual copies of road geometry by collecting and processing trajectory data from multiple vehicles. Instead of traditional surveying methods, the system reconstructs HD map information by aggregating position data, lane markings, and road features observed by equipped vehicles, thereby reducing both time and cost while maintaining high precision
Solution Approach 2:
The system serves multiple purposes simultaneously: it collects trajectory data for navigation, generates HD map information for cartography, and provides real-time road condition updates. This multi-functional approach allows the same data collection infrastructure to produce HD maps without requiring separate dedicated mapping operations
2Use of energy by moving object
If SD maps are used to reduce computing capacity and memory requirements, then device resource consumption is reduced, but map-matching accuracy deteriorates
Solution Approach 1:
The patent generates local HD maps for specific geographic areas rather than maintaining global HD maps. This allows vehicles to have high-precision map data for their current location while using standard definition maps for other areas, optimizing the balance between memory usage and matching accuracy based on local needs
3Loss of information
If traditional mapping methods are used, then comprehensive map coverage is achieved, but ability to capture real-time changes and temporary disruptions is lost
Solution Approach 1:
The system continuously receives trajectory data from vehicles and uses this feedback to update the HD map in real-time. When road conditions change (e.g., temporary disruptions, construction, accidents), the aggregated vehicle data provides feedback that triggers map updates, ensuring the map reflects current conditions without requiring manual intervention
Solution Approach 2:
The HD map system updates itself automatically by processing vehicle trajectory data without requiring external mapping operations. The vehicles themselves serve as mobile sensors that continuously provide data for map maintenance and updates, enabling the system to self-update in response to changing road conditions
Data Source
AI summary
A method for assembling a local high-definition map comprises receiving path histories. Each path history comprises a set of location points that an entity consecutively traversed. The method further comprises constructing, based on the path histories, an initial undirected graph comprising location nodes, intersection nodes and edges. Each location node corresponds to one of the location points. Each intersection node corresponds to an intersection point of two of the path histories. Edges are defined between location nodes corresponding to adjacent location points of one of the path histories, and, in case of an intersection node, edges are defined between the intersection node and location nodes corresponding to location points of each of the respective two path histories between which location points the respective intersection point lies. The method further comprises generating, in a reduction step, a reduced graph by removing redundancies of the received path histories.