Hierarchical Target Selection for Vehicle Countermeasure Systems
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Solution Overview
Problem
Current collision warning and countermeasure systems face high computational demands due to the need to sequentially process detected objects against various target selection requirements, making them infeasible for automotive applications and lacking accuracy in path prediction and countermeasure activation.
Innovation Solution
A target selection system that includes an object detection sensor generating signals for multiple detected objects, a feature target selection module selecting secondary targets, and a primary target selection module associating these targets with respective features, along with a path prediction module estimating a vehicle's future path and confidence level, to improve target evaluation and countermeasure deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sequential processing of detected objects against target selection requirements is performed, then target evaluation accuracy is improved, but computational demand increases making the system infeasible for automotive applications
Solution Approach 1:
The patent segments the target selection process into multiple hierarchical levels: initial object detection, secondary target selection based on basic criteria, and primary target selection for final countermeasure activation. This segmentation allows the system to process multiple objects without requiring full sequential analysis of each object against all target selection requirements, thereby reducing computational demand while maintaining evaluation accuracy for the most critical targets.
Solution Approach 2:
The patent applies local quality by allocating different processing depths to different targets based on their threat level and relevance. Primary targets receive full analytical processing, secondary targets receive abbreviated processing, and other objects receive minimal processing. This differential processing strategy maintains high evaluation accuracy for critical targets while significantly reducing overall computational demand.
2Productivity
If high-powered processors are used to handle computational demand, then processing capability is improved, but system cost increases making it infeasible for automotive applications
Solution Approach 1:
The patent segments processing tasks across multiple controllers with different computational capabilities. The main controller performs high-level target selection and countermeasure coordination, while secondary controllers handle specific sensor processing and target analysis. This segmentation enables the system to achieve high processing capability through coordinated distributed processing rather than requiring a single high-powered processor, thereby reducing system cost.
Solution Approach 2:
The patent designs controllers with multi-functionality to perform multiple tasks: object detection, target selection, path prediction, and countermeasure control. By making controllers universal rather than specialized, the system achieves high processing capability through efficient resource utilization across multiple components, reducing the need for expensive dedicated high-powered processors.
3Measurement precision
If path prediction accuracy is improved for countermeasure activation, then countermeasure deployment accuracy is improved, but processing requirements increase
Solution Approach 1:
The patent performs preliminary path prediction and target selection before countermeasure activation is required. By continuously predicting object paths and pre-identifying potential targets, the system maintains accurate path information ready for immediate countermeasure deployment. This preliminary action enables high path prediction accuracy without increasing processing time during critical activation moments.
Solution Approach 2:
The patent implements continuous path prediction and target tracking rather than intermittent processing. Sensors continuously monitor object positions and the system continuously updates path predictions, maintaining accurate predictive information without requiring intensive batch processing. This continuous useful action ensures high path prediction accuracy while distributing processing load over time, avoiding time-critical processing bottlenecks.
Data Source
AI summary
A target selection system (21) for a vehicle (12) includes an object detection sensor (17) that generates object detection signals associated with multiple detected objects. A feature target selection module (30) selects secondary targets from the objects and associates the secondary targets with respective features (23). A primary target selection module (31) selects primary targets from the secondary targets and associates each of the primary targets with a single respective concentrated feature.