Lane-Level Traffic Prediction Using Geographic Database Patterns
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Solution Overview
Problem
Current digital maps for autonomous vehicles and advanced driver-assistance systems lack lane-level traffic information, especially when network connections are unavailable or bandwidth is limited, hindering accurate navigation functions.
Innovation Solution
The method involves generating and incorporating lane direction patterns, divergent parameter patterns, and lane event patterns into a geographic database, using probe data from vehicles to predict traffic conditions and travel times, even without real-time information, by grouping and analyzing data into epochs and segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time lane-level traffic information is provided via network connection, then navigation accuracy is improved, but network bandwidth consumption increases and reliability decreases when connection is unavailable
Solution Approach 1:
The system pre-calculates and stores traffic parameter predictions (travel times, speeds, traffic conditions) for multiple road segments and time epochs in the geographic database before network connection is needed. This preliminary action ensures that when the vehicle operates offline, pre-computed lane-level traffic information is immediately available without requiring real-time network data.
Solution Approach 2:
The system creates simplified copies of traffic information by storing aggregated traffic parameters (average speeds, travel times, traffic conditions) for different epochs and road segments in the geographic database. These copied data structures serve as offline substitutes for real-time network data, maintaining navigation functionality when network connection is unavailable.
2Measurement precision
If detailed lane-level traffic data is transmitted over the network, then navigation precision is improved, but network bandwidth requirements increase
Solution Approach 1:
The system extracts only the essential traffic parameters (travel time, average speed, traffic condition indicators) needed for navigation decisions and stores them in compressed formats in the geographic database. By extracting only critical information rather than transmitting complete raw probe data, the system reduces network bandwidth requirements while maintaining sufficient precision for lane-level navigation.
Solution Approach 2:
The system segments traffic data by road segments, time epochs, and traffic conditions, storing predictions for specific segments and time periods in the geographic database. This segmentation allows the system to transmit and store only relevant data for particular routes and time windows rather than comprehensive real-time data for all road networks, significantly reducing data transmission volume.
3Measurement precision
If historical probe data from multiple vehicles is aggregated and analyzed, then traffic parameter prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system merges probe data from multiple vehicles by aggregating travel times, speeds, and traffic conditions into unified statistical distributions for each road segment and epoch. By combining multiple data sources into consolidated traffic parameter predictions stored in the geographic database, the system improves prediction accuracy through larger sample sizes while managing complexity through standardized aggregation algorithms.
Solution Approach 2:
The system transforms raw probe data into standardized traffic parameters (average speed, travel time, traffic condition categories) and stores these transformed parameters in the geographic database. By changing the parameter representation from raw vehicle telemetry to aggregated traffic metrics, the system simplifies subsequent prediction operations while maintaining high prediction accuracy through statistically robust parameter transformations.
Data Source
AI summary
A plurality of instances of event data are received. Each instance of event data corresponds to a traffic event on a first segment and comprises an indication of at least one parameter characterizing travel of a vehicle along the first segment during the traffic event. Based on the plurality of instances of event data, a recurring event is identified. At least one representative parameter characterizing travel along the first segment during the recurring event is determined based on the two or more instances of event data corresponding to the recurring event. At least one element of a data structure is modified with the at least one representative parameter. The at least one element of the data structure corresponds to the first segment. A geographic database comprises the data structure and a navigation application is configured to use at least a portion of the data structure to perform a navigation function.