Dynamic Ellipse Collision Avoidance for Host Vehicles
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Solution Overview
Problem
Current vehicle collision avoidance systems face challenges in predicting and mitigating collisions with multiple target vehicles, as they often rely on static or simplistic geometric models that fail to account for dynamic changes in vehicle paths and relative positions.
Innovation Solution
A system that generates an ellipse around a target vehicle based on its heading angle, length, width, and speed difference, predicting future paths and actuating vehicle components to avoid tangent lines representing potential collision zones, using a path optimization program to continuously adjust and update avoidance maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static or simplistic geometric models are used for collision prediction, then the system complexity is reduced, but the accuracy of collision avoidance deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static geometric models to dynamic predictive models that continuously update collision risk assessments based on real-time vehicle paths, speeds, and positions. The system dynamically adjusts safety margins and collision probability calculations as vehicles move, enabling accurate collision prediction while managing computational complexity through efficient algorithms.
Solution Approach 2:
The system changes parameters by incorporating multiple variables including vehicle speed, heading angle, path curvature, and relative position into the collision prediction model. By dynamically adjusting these parameters based on real-time sensor data and predicted trajectories, the system achieves high accuracy in collision prediction without requiring overly complex system architecture.
2Measurement precision
If dynamic predictive models are used to account for changing vehicle paths and positions, then collision prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the collision prediction task into distinct modules: path prediction, collision risk assessment, and safety determination. Each module processes specific aspects of the problem independently, allowing the system to achieve high prediction accuracy through specialized algorithms while managing overall complexity through modular architecture and clear separation of concerns.
3Reliability
If the system continuously updates and adjusts avoidance maneuvers using path optimization, then collision avoidance effectiveness is improved, but computational time and processing requirements increase
Solution Approach 1:
The system applies preliminary action by pre-calculating safety margins and collision risk thresholds before actual collision scenarios occur. The path optimization algorithm prepares multiple potential avoidance trajectories in advance, and when a collision risk is detected, the system can immediately execute a pre-planned maneuver, significantly reducing computational time while maintaining high avoidance effectiveness.
Solution Approach 2:
The patent implements feedback by continuously monitoring vehicle positions, speeds, and path deviations, then using this real-time data to adjust collision risk assessments and update avoidance maneuvers. The system compares actual vehicle behavior against predicted paths and dynamically refines safety determinations, ensuring high reliability while optimizing computational efficiency through iterative rather than exhaustive calculations.
Data Source
AI summary
A computer includes a processor and a memory, the memory storing instructions executable by the processor to generate an ellipse around a target vehicle, identify an intersection point between the ellipse and a line extending from a host vehicle to the target vehicle, identify a line tangent to the ellipse at the intersection point, and actuate one or more components of the host vehicle to avoid locations represented by the line tangent to the ellipse.