Vehicle Path Prediction Confidence System
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Solution Overview
Problem
Existing collision warning and countermeasure systems face challenges in accurately predicting the future path of a vehicle and determining the confidence level of these predictions, which affects the effectiveness and decision-making for countermeasure activation.
Innovation Solution
A path prediction system that utilizes vehicle state sensors and path-tracking sensors to generate multiple estimations of a vehicle's future path, determining a resultant predicted path and associated confidence level, which is used by a controller to decide on countermeasure activation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors and estimation methods are used to improve path prediction accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The path prediction system is divided into multiple independent estimation modules, each using different sensor data (vehicle state sensors, path-tracking sensors) to generate separate path predictions. This segmentation allows each module to specialize in specific sensing tasks while maintaining overall system accuracy without requiring a single complex monolithic system.
Solution Approach 2:
Multiple path predictions from different estimation modules are merged through a confidence level calculation mechanism. The system combines predictions from vehicle state sensors and path-tracking sensors, weighting them according to their respective confidence levels, thereby achieving improved measurement precision through systematic integration of multiple data sources.
2Reliability
If confidence level calculation is added to path prediction, then reliability of countermeasure activation improves, but device complexity increases
Solution Approach 1:
The system implements a feedback mechanism where confidence levels are continuously calculated based on the agreement between multiple path predictions. This confidence level feedback is then used by the countermeasure system to adjust activation decisions, creating a closed-loop control system that improves reliability without requiring complex external validation systems.
Solution Approach 2:
The confidence level calculation is performed in advance of countermeasure activation decisions. By preliminarily assessing the reliability of path predictions through confidence level computation, the system prepares reliable decision-making data before countermeasure activation is required, ensuring reliable activation without adding complexity to the critical activation moment.
3Measurement precision
If multiple path estimations are generated and compared, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system generates multiple path estimations using different sensor combinations and estimation methods, performing more computations than a single estimation would require. This partial or excessive action in generating multiple estimations is justified by the significant improvement in measurement precision achieved through comparing and weighting these estimations according to their confidence levels.
Data Source
AI summary
A path prediction system (10) for a vehicle (12) includes vehicle state sensors (18) that generate vehicle state signals. A tracking sensor (20) generates a path characteristic signal. A path prediction module (16) determines predicted path estimations in response to data received from each of the vehicle state sensors (18) and the tracking sensor (20). The path prediction module (16) determines a resultant predicted future path and a path confidence level in response to the predicted path estimations. A controller (14) performs a countermeasure (26) in response to the resultant predicted future path and the path confidence level.