Ramp Traffic Simulation Using Virtual Vehicles in Sensor Gaps
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Solution Overview
Problem
Current data processing methods for simulating traffic status on roads struggle to accurately reproduce and predict traffic conditions on ramps without sensed data, leading to reduced accuracy in simulating and managing physical road traffic.
Innovation Solution
A data processing method and apparatus that generates virtual simulated vehicles in sensing blank regions based on historical data and autonomous driving models, allowing for accurate reproduction and prediction of traffic status on ramps by integrating these vehicles into the simulation system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If virtual simulated vehicles are generated in sensing blank regions, then the accuracy of reproducing and predicting traffic status on ramps is improved, but the complexity of the simulation system increases
Solution Approach 1:
The system performs preliminary actions by generating virtual simulated vehicles in sensing blank regions before actual traffic simulation begins. This ensures that ramps without sensor coverage are pre-populated with virtual vehicles that follow autonomous driving models, allowing the system to reproduce and predict traffic status accurately without waiting for real sensor data to arrive.
Solution Approach 2:
Virtual simulated vehicles act as intermediaries between sensing blank regions and the traffic simulation system. These virtual vehicles bridge the gap where physical sensors cannot provide data, enabling the system to maintain continuous and accurate traffic status reproduction and prediction across all road segments including ramps without direct sensor coverage.
2Measurement precision
If virtual simulated vehicles are generated in sensing blank regions, then the accuracy of predicting traffic status on ramps is improved, but the computational resources required increase
Solution Approach 1:
The system applies local quality by generating virtual simulated vehicles specifically in sensing blank regions rather than uniformly across all road segments. This targeted approach concentrates computational resources only where sensor data is unavailable, maintaining high prediction accuracy for ramps while avoiding unnecessary computational overhead in regions already covered by physical sensors.
3Reliability
If autonomous driving models are used to control virtual simulated vehicles, then the realism of traffic status simulation is improved, but the complexity of the system increases
Solution Approach 1:
The system uses copying by creating virtual simulated vehicles that replicate the behavior patterns of real vehicles through autonomous driving models. These virtual copies follow the same decision-making algorithms and driving behaviors as physical vehicles, ensuring realistic traffic status simulation without requiring actual physical vehicles or drivers in the simulation environment.
Data Source
AI summary
A data processing method includes: determining, in a driving simulation system, whether a simulated ramp connected to a simulated main road belongs to a sensing region with sensed data; generating a first virtual simulated vehicle in the simulated ramp at a simulation starting moment if the simulated ramp does not belong to the sensing region with the sensed data; controlling, in a simulation reproduction stage, a driving behavior of at least one second virtual simulated vehicle traveling in the simulated ramp, to obtain a traffic status of the simulated ramp; and controlling, in a simulation prediction stage, based on the traffic status of the simulated ramp, a driving behavior of a third virtual simulated vehicle traveling in the simulated ramp, to obtain a predicted traffic status of the simulated ramp, the predicted traffic status being configured to control a traveling state of a physical vehicle traveling on a physical road.