Lane Level Traffic State Estimation Using Probe Vehicle Data
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Solution Overview
Problem
Current traffic monitoring systems lack accurate lane-level traffic congestion metrics, especially in scenarios with low vehicle-to-vehicle (V2V) penetration rates and areas where vehicle data is unavailable, leading to incomplete traffic assessments.
Innovation Solution
A computer-implemented method and system that utilize probe vehicles to collect and spatially associate data at lane level, identify empty cells, and calculate estimated data for non-probe vehicles, thereby providing comprehensive traffic density values to host vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only V2V equipped vehicles are used for lane speed monitoring, then the system complexity is reduced, but the measurement precision deteriorates due to lack of compensation for road segments with no V2V equipped vehicles
Solution Approach 1:
The patent introduces infrastructure devices (roadside units, cameras, radar systems) as intermediaries to bridge the gap between V2V equipped vehicles and non-equipped vehicles. These infrastructure devices collect data from all vehicles including non-V2V equipped ones, enabling accurate lane speed monitoring across all road segments without requiring every vehicle to have V2V capabilities.
Solution Approach 2:
The system creates a universal monitoring approach that works for both V2V equipped and non-equipped vehicles. By combining V2V data with infrastructure-based data collection, the system achieves multi-functionality in monitoring all vehicle types and road segments, eliminating the limitation of relying solely on V2V equipped vehicles.
2Measurement precision
If infrastructure positioned near the roadway is used for traffic monitoring, then the measurement precision is improved, but the device complexity and installation requirements increase
Solution Approach 1:
The patent merges V2V communication capabilities with infrastructure-based monitoring systems. By combining data from both sources, the system achieves high measurement precision while distributing the complexity across multiple components rather than relying on a single complex infrastructure system.
Solution Approach 2:
Instead of requiring complete infrastructure coverage for all road segments, the system uses partial infrastructure deployment combined with V2V data. This allows the system to achieve sufficient measurement precision for most applications while reducing the overall complexity and installation requirements compared to full infrastructure coverage.
3Ease of operation
If average velocity from V2V equipped vehicles is used to determine traffic congestion levels, then the ease of operation is improved, but the measurement precision deteriorates in low penetration rate scenarios
Solution Approach 1:
The patent uses infrastructure devices as intermediaries to collect traffic data from non-V2V equipped vehicles, enabling accurate traffic congestion assessment even in low penetration rate scenarios where V2V equipped vehicles are sparse. This intermediary approach ensures that the absence of V2V data from certain vehicles does not compromise the overall measurement precision.
Solution Approach 2:
The system incorporates feedback mechanisms where infrastructure data complements and corrects V2V data. By continuously comparing and adjusting measurements based on both V2V and infrastructure sources, the system maintains high measurement precision for traffic congestion levels regardless of V2V penetration rate.
Data Source
AI summary
A method for traffic state estimation of a road network based on a plurality of vehicles including probe vehicles and non-probe vehicles travelling includes receiving probe vehicle data from the probe vehicles within a communication range of a host vehicle. The method also includes spatially and temporally associating the probe vehicle data to lane level cells of the road network, and identifying empty lane level cells of the road network where the probe vehicle data is unavailable. The method includes calculating estimated non-probe vehicle data for the empty lane level cells based on the probe vehicle data. The method further includes calculating a traffic density value for the road network based on the probe vehicle data and the estimated non-probe vehicle data and providing the traffic density value to the host vehicle.