Connected Vehicle Traffic Prediction for CAV Control Signals
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Solution Overview
Problem
Traffic congestion poses significant costs in terms of lost person-hours, excess carbon emissions, and increased vehicle accidents, and existing technologies have not effectively leveraged connected vehicles (CVs) and connected automated vehicles (CAVs) to mitigate these issues.
Innovation Solution
A method and apparatus that generate and transmit control signals to connected autonomous vehicles (CAVs) using a long-term shared world model, which is created by accessing vehicle data from a subset of CVs and applying a traffic flow model to predict future velocities on a roadway portion, thereby optimizing vehicle operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time control signals are transmitted to CAVs based on current traffic data, then responsiveness and control accuracy are improved, but the system cannot predict future traffic conditions leading to suboptimal decision-making
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing traffic data from connected vehicles in a long-term shared world model before control decisions are needed. This pre-processing of traffic data allows the system to quickly generate accurate predictions when control signals are required, resolving the contradiction between prediction accuracy and computational time.
Solution Approach 2:
The system dynamically adjusts the prediction horizon and model complexity based on current traffic conditions and computational resources available. When traffic conditions are stable, simpler models are used; when conditions change rapidly, more complex predictions are generated with extended time horizons, optimizing the balance between accuracy and computational efficiency.
2Measurement precision
If extensive vehicle data is collected from multiple CVs to improve prediction accuracy, then traffic flow prediction precision is improved, but data processing complexity and computational load increase
Solution Approach 1:
The system extracts only the essential features from raw vehicle data (position, velocity, acceleration, heading) and stores them in a structured long-term shared world model. By filtering and extracting only relevant parameters needed for prediction, the system reduces data processing complexity while maintaining prediction accuracy.
Solution Approach 2:
The system transforms raw vehicle data into standardized parameters (relative positions, velocities, accelerations, headings) and organizes them in a consistent temporal and spatial framework. This parameter transformation simplifies subsequent prediction computations while preserving all necessary information for accurate traffic flow forecasting.
3Productivity
If control signals are generated based on long-term predictions, then traffic congestion reduction is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The control system is segmented into modular components: data collection from connected vehicles, long-term shared world model construction, prediction engine for future traffic states, and control signal generation. Each module performs a specific function independently, reducing overall system complexity while enabling sophisticated long-term predictions for optimizing roadway throughput.
Data Source
AI summary
A server accesses vehicle data from each connected vehicle (CV) of a subset of a plurality of CVs on a roadway portion, the vehicle data comprising at least one of: a position, a velocity, or a headway. The server generates, based on the accessed vehicle data, a long-term shared world model. The server generates, using the long-term shared world model, a data structure representing predicted future velocities on the roadway portion by position and time by applying a traffic flow model to the long-term shared world model. The server transmits, to a connected autonomous vehicle (CAV), a control signal for controlling operation of the CAV based on the generated data structure.