Dual-Assessment Collision Warning Using Segmented Cones and Bayesian Filtering
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Solution Overview
Problem
Current collision warning systems face challenges in processing large amounts of real-time data from various sources to accurately predict collisions and provide effective warnings, especially with the increasing complexity of sensor information from vehicles, pedestrians, and intelligent infrastructure.
Innovation Solution
A dual-assessment system that employs a preliminary assessment mechanism for rapid geometric identification of potential accidents and a specialized assessment mechanism for detailed statistical analysis, using Bayesian filtering and segmented cones to predict future movements and positions of principals, thereby facilitating early and accurate collision warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a collision warning system processes a huge amount of information from multiple sources (sensors, intelligent intersections, peer vehicles), then the accuracy of collision prediction improves, but the computational complexity and processing time increase significantly
Solution Approach 1:
The system segments the information processing task into two distinct assessment mechanisms: a preliminary assessment mechanism that performs rapid geometric identification of potential collisions using simple cone representations, and a specialized assessment mechanism that conducts detailed statistical analysis using Bayesian filtering. This segmentation allows the system to handle large volumes of sensor data efficiently by applying appropriate processing complexity only where needed.
Solution Approach 2:
The preliminary assessment mechanism performs initial filtering and geometric analysis before the specialized assessment mechanism processes data in detail. By conducting preliminary geometric identification using segmented cones and long-range dynamics models, the system eliminates obviously non-collision scenarios early, reducing the computational burden on subsequent detailed analysis while maintaining high prediction accuracy.
2Loss of time
If the system provides early collision warnings by analyzing long-range dynamics, then the warning time is extended, but the computational resources required for accurate prediction increase
Solution Approach 1:
The system employs dynamics models that adapt their complexity based on the assessment stage. The preliminary assessment uses simplified long-range dynamics models for early warning detection, while the specialized assessment uses more computationally intensive Bayesian filtering only when needed. This dynamic adjustment of model complexity extends warning time while controlling energy consumption.
Solution Approach 2:
The system changes processing parameters between assessment stages: the preliminary assessment uses geometric parameters and simplified dynamics models for efficient long-range prediction, while the specialized assessment transitions to statistical parameters and comprehensive Bayesian filtering. This parameter transformation allows early warning with controlled computational resource usage.
3Measurement precision
If the system uses detailed statistical analysis for all potential collisions, then the accuracy of collision risk assessment improves, but the processing speed decreases
Solution Approach 1:
The assessment system is segmented into two stages with different accuracy-speed characteristics. The preliminary assessment provides rapid geometric screening at lower accuracy, while the specialized assessment provides detailed statistical analysis at higher accuracy only for scenarios that pass the preliminary filter. This segmentation ensures high processing speed for the majority of non-collision scenarios while maintaining high accuracy for potential collision detection.
Solution Approach 2:
The system applies detailed statistical analysis (Bayesian filtering) partially - only to scenarios identified as potential collisions by the preliminary geometric assessment. This partial application of complex analysis maintains high processing speed for overall system operation while achieving high accuracy for critical collision risk assessments.
Data Source
AI summary
One embodiment of the present invention provides a system that for facilitating assessment of collision between a primary principal and a non-primary principal for early warning. During operation, the system periodically performs the following operations: The system obtains a current observation of the primary principal and non-primary principal. The system then assesses one or more future states for the primary and non-primary principals, respectively, based on: the current observation of the primary and non-primary principals, a dynamics model of the primary principal, and a mental-state model of a person associated with the primary principal. The system further produces one or more results which indicate an assessment of collision between the primary and non-primary principals.