Connected Vehicle Identification With Context-Adaptive Search Parameters
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Solution Overview
Problem
Existing vehicle-to-vehicle communication systems struggle to accurately identify connected vehicles in varying traffic scenarios due to the use of fixed parameters, leading to inefficiencies and potential delays in message exchange.
Innovation Solution
A dynamic tuning mechanism adjusts parameters such as search radius, number of iterations, and weight factors in the cost function based on real-time sensor data and environmental context, enhancing the accuracy of connected vehicle detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed parameters are used for vehicle identification, then device complexity is reduced, but measurement precision and detection accuracy deteriorate
Solution Approach 1:
The patent implements dynamic parameter adjustment where search radius, iteration count, and cost function weights are adapted in real-time based on traffic density, vehicle speed, and environmental conditions. This transforms the static parameter system into a dynamic one that automatically optimizes detection accuracy for varying traffic scenarios without requiring manual reconfiguration.
Solution Approach 2:
The system changes operational parameters (search radius, number of iterations, weight factors) based on detected traffic conditions. When traffic density increases, the search radius is reduced and iteration count is increased to maintain detection precision, while in low-density conditions, parameters are relaxed to reduce computational load.
2Ease of operation
If fixed parameters are used for message exchange, then ease of operation is improved, but productivity and response time deteriorate
Solution Approach 1:
The system performs self-optimization by automatically adjusting its own parameters based on real-time sensor data and traffic conditions. The vehicle's processor autonomously determines optimal search parameters and cost function weights without external intervention, maintaining ease of operation while maximizing message exchange efficiency through context-aware parameter selection.
Solution Approach 2:
The system incorporates feedback loops where detection results and traffic condition monitoring continuously inform parameter adjustments. The cost function uses feedback from successful/detected vehicles to refine weight factors, and search parameters are adjusted based on feedback from sensor data quality and message exchange outcomes.
3Measurement precision
If comprehensive sensor data processing is performed, then measurement precision improves, but use of energy and computational load increase
Solution Approach 1:
The system applies partial processing by selectively focusing computational resources on the most relevant sensor data based on current traffic conditions. The cost function prioritizes processing of critical parameters (position, speed, acceleration) while using reduced precision for less critical attributes, achieving sufficient detection accuracy with lower energy consumption than full-comprehensive processing would require.
Data Source
AI summary
An example operation includes one or more of identifying, via an ego vehicle, one or more surrounding vehicles of the ego vehicle based on sensor data from the ego vehicle, determining a state of an ego vehicle and a state of the one or more surrounding vehicles of the ego vehicle, dynamically determining parameters for identifying connected vehicles based on the determined states of the ego vehicle and the one or more surrounding vehicles, and detecting a connected vehicle from among the one or more surrounding vehicles via an exchange of messages between the ego vehicle and the one or more surrounding vehicles based on the dynamically determined parameters.