Collision Avoidance System Target Segmentation
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Solution Overview
Problem
Implementing collision mitigation between a host vehicle and multiple targets at intersections is computationally costly and difficult due to the need for data from multiple sensors and complex threat assessments, especially when targets have varying collision risks.
Innovation Solution
A system that determines confidence levels for potential collisions based on targets' heading angles and distances, filters targets to reduce computational load, and actuates vehicle components based on threat numbers, distinguishing between longitudinally oncoming and laterally moving targets to prioritize threat assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If threat assessment is performed for all detected targets using multiple sensors, then collision detection reliability is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent segments the target set into different categories based on collision risk: high-risk targets (requiring full threat assessment with multiple sensors) and low-risk targets (requiring reduced assessment). This segmentation allows the system to apply different levels of computational resources to different targets, improving overall efficiency while maintaining reliability for critical targets.
Solution Approach 2:
The patent applies local quality by using different assessment strategies for different targets based on their specific characteristics. High-risk targets receive comprehensive multi-sensor threat assessment, while low-risk targets receive simplified assessment. This localized approach optimizes computational resources where they are most needed.
2Measurement precision
If comprehensive threat assessment is performed for multiple targets simultaneously, then collision prevention accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent segments targets by collision risk level and processes them through different assessment pipelines. High-risk targets undergo comprehensive threat assessment for accuracy, while low-risk targets use expedited processing. This segmentation resolves the contradiction by applying full precision only where necessary.
Solution Approach 2:
The patent applies partial action by performing complete threat assessment only for high-risk targets rather than all targets. For low-risk targets, a reduced assessment is sufficient, eliminating excessive computational effort while maintaining adequate accuracy for collision prevention.
3Reliability
If data from multiple sensors is collected for each target, then threat assessment reliability is improved, but system resource consumption increases
Solution Approach 1:
The patent segments the assessment process into full multi-sensor evaluation for high-risk targets and reduced evaluation for low-risk targets. This segmentation ensures reliable threat assessment is performed only when necessary, reducing overall energy consumption while maintaining reliability for critical situations.
Solution Approach 2:
The patent changes the parameter of sensor data collection based on target risk level. For high-risk targets, all sensors are activated for comprehensive data collection. For low-risk targets, fewer sensors are used or data collection is reduced, optimizing energy consumption while maintaining adequate assessment reliability.
Data Source
AI summary
A respective confidence level of a potential collision is determined for each of a plurality of targets based on each target's heading angle and distance from a host vehicle. A threat number is determined for each target when its respective confidence level is above a threshold. A vehicle component is actuated based on the threat number.