Multi-Lane Collision Prediction via Vehicle Convergence Analysis
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Solution Overview
Problem
Existing vehicle systems primarily focus on adjacent lanes and often fail to detect potential collisions between vehicles changing lanes, particularly in multi-lane roadways, leading to a lack of situational awareness for intermediate lane interactions.
Innovation Solution
A method and system that determine the convergence of host and target vehicles from different lanes onto an intermediate lane by evaluating relationships between vehicle separations, speeds, and geometries using inequalities, and predict collisions based on these evaluations, incorporating sensors like radar, lidar, and communication systems for accurate position and speed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vehicle monitoring systems focus on adjacent lanes only, then system complexity is reduced, but collision detection capability in multi-lane scenarios deteriorates
Solution Approach 1:
The system segments the monitoring task by identifying specific convergence scenarios involving intermediate lanes, rather than attempting to monitor all possible lane configurations simultaneously. This allows the system to focus computational resources on detecting convergence patterns between vehicles in adjacent and intermediate lanes, improving detection capability without proportionally increasing overall system complexity.
Solution Approach 2:
The system adds a temporal dimension to lane monitoring by evaluating convergence over time periods. Instead of only checking static lane positions, the system monitors changes in vehicle positions and lane assignments over time to identify convergence patterns, enabling detection of intermediate lane collisions without requiring continuous monitoring of all spatial dimensions simultaneously.
2Measurement precision
If the system monitors all lanes for convergence, then collision prediction accuracy improves, but computational load increases
Solution Approach 1:
The system performs preliminary identification of vehicles that may converge into intermediate lanes before the actual convergence occurs. By detecting early indicators such as lane change intentions or positioning patterns, the system can pre-flag potential convergence scenarios, allowing for more accurate collision prediction while reducing computational load by focusing detailed analysis only on pre-identified high-risk scenarios.
3Loss of information
If the system evaluates multiple convergence conditions, then situational awareness improves, but response time deteriorates
Solution Approach 1:
The system maintains continuous monitoring of vehicle positions and lane assignments, updating convergence evaluations in real-time as vehicles move. This continuous action allows the system to maintain comprehensive situational awareness across multiple conditions without requiring periodic batch processing, thereby reducing response time while preserving detailed situational information.
Data Source
AI summary
A system and method for predicting a collision between a host vehicle and a target vehicle operating on a multi-lane roadway may include determining the host and target vehicles are converging from respective first and second lanes to a third lane intermediate the first and second lanes. A predetermined set of conditions is evaluated including relationships among the host and target vehicle separations and speeds, and a collision predicted based upon the evaluation.