Vehicle Path Generation Using Frequency-Based Entry Exit Detection
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Solution Overview
Problem
Mapping service providers face challenges in generating accurate vehicle paths from data on a limited graph network, leading to reduced accuracy in traffic conditions and user trust issues due to interpolation of trivial or unlikely vehicle paths.
Innovation Solution
A method and apparatus that determine the expected frequency of location data from vehicles, detect exits or entries on the roadway, and initiate the creation, breaking, or modification of vehicle paths based on frequency comparisons, ensuring accurate path generation even with incomplete data coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicle paths are interpolated from limited location data on a roadway graph network, then path generation can proceed with incomplete data coverage, but the accuracy of vehicle paths and traffic conditions is significantly reduced due to interpolation of trivial or highly unlikely paths
Solution Approach 1:
The system continuously monitors the frequency of location data from vehicles and uses this feedback to dynamically adjust path generation. By comparing expected frequency (based on historical data) with observed frequency, the system identifies when vehicles have exited or entered the graph network, allowing it to break or create paths accordingly. This feedback mechanism ensures paths are generated only when sufficient data is available, maintaining accuracy while enabling path generation with limited coverage.
Solution Approach 2:
The path generation system transitions from a static approach to a dynamic one by continuously adjusting path validity based on real-time frequency analysis. Paths are not fixed but are dynamically created, broken, or modified as vehicles enter or exit the graph network. This dynamic adaptation allows the system to maintain high path accuracy by only interpolating paths when location data frequency indicates actual vehicle presence, rather than relying on static assumptions about path validity.
2Measurement precision
If the coverage of the roadway graph network is expanded to capture more vehicle data points, then path accuracy improves, but the complexity and cost of maintaining comprehensive graph coverage increases
Solution Approach 1:
The system applies partial action by generating paths only for specific road segments where sufficient location data is actually observed, rather than attempting to maintain comprehensive coverage of the entire graph network. By using frequency comparison to identify where vehicles are actually present, the system performs path generation only where needed, achieving high path accuracy for monitored segments without the complexity of maintaining full network coverage.
Solution Approach 2:
The system uses the vehicles' own location data to automatically determine where path generation is needed, eliminating the need for manual graph coverage planning. The frequency-based detection mechanism allows the system to self-identify road segments requiring path analysis based on actual vehicle presence, rather than requiring pre-defined comprehensive coverage areas. This self-service approach reduces graph maintenance complexity while maintaining accuracy where data exists.
3Reliability
If frequency-based detection is used to identify vehicle exits and entries, then false paths can be identified and corrected, but the complexity of analyzing location data frequency increases
Solution Approach 1:
The system changes the parameter used for path validation from simple presence/absence to frequency-based analysis. By monitoring the frequency of location data and comparing it against expected frequency thresholds, the system can reliably detect when vehicles have exited or entered the graph network. This parameter change enables automatic identification of false paths through frequency anomalies, improving path validity while keeping the analysis complexity manageable through threshold-based decision rules.
Data Source
AI summary
An approach is provided for generating/breaking vehicle paths on a limited graph area. The approach, for example, determining an expected frequency of location data collected from a sensor of a vehicle traveling on a roadway, wherein the location data include a plurality of probe points that are time-sequenced. The approach also involves detecting an exit or an entry of the vehicle on the roadway based on comparing the expected frequency to an observed frequency of the location data on at least one portion of the roadway. The approach further involves initiating an identification, a creation, a breaking, or a combination thereof of a path constructed from the location data based on the detecting of the exit or the entry of the vehicle. The approach further involves providing the path as an output.