Weighted Confidence Estimation for Driving Assistance
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Solution Overview
Problem
Existing driving assistance systems face challenges in reliability due to detection inaccuracies and errors from sensor limitations, leading to potentially confusing or unsafe system responses.
Innovation Solution
A method for a driving assistance system that uses weighted confidence estimates to improve prediction reliability by associating weights with basic confidence estimates based on detection error impacts, allowing for more accurate assessments and reduced probabilities of wrong predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional sensor equipment is provided to enhance system reliability, then detection accuracy and prediction reliability improve, but hardware complexity and costs increase
Solution Approach 1:
The patent changes the parameter of confidence estimation by introducing weighted confidence estimates that account for detection errors. Instead of adding sensors, the system modifies how existing sensor data is evaluated by associating weights with basic confidence estimates based on the impact of potential detection errors, thereby improving prediction reliability through parameter optimization rather than hardware expansion
Solution Approach 2:
The patent creates a virtual model of detection uncertainty by generating weighted confidence estimates that replicate the effect of having multiple sensor sources. The weighting mechanism copies the reliability assessment function that would otherwise require additional sensor equipment to provide, allowing the system to evaluate prediction reliability without physical duplication of sensing capabilities
2Measurement precision
If additional sensor equipment is provided to enhance system reliability, then detection accuracy improves, but costs increase
Solution Approach 1:
The patent improves detection accuracy by changing the parameter of confidence weighting. By associating different weights with basic confidence estimates based on detection error impact, the system optimizes the use of existing sensor data to achieve higher effective detection accuracy without the cost of additional sensors
3Ease of operation
If sensor data inaccuracies are not considered, then system operation is simpler, but prediction reliability deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-calculating and associating weights with basic confidence estimates before predictions are made. The system prepares confidence weighting information in advance based on detection error characteristics, so that when predictions are generated, the reliability assessment is already embedded in the weighted confidence estimates, maintaining operational simplicity while improving reliability
Solution Approach 2:
The patent introduces feedback by using detection error information to adjust confidence estimates. The system feeds back knowledge about potential sensor inaccuracies into the confidence calculation process, allowing predictions to be weighted according to their expected reliability, thereby improving prediction accuracy without complicating the core prediction logic
Data Source
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AI summary
The invention relates to a driving assistance system (100) including a prediction subsystem (110) in a vehicle. According to a method aspect of the invention, the method comprises the steps of accepting a set of basic environment representations (120); allocating a set of basic confidence estimates (122); associating weights to the basic confidence estimates (122); calculating (128) a weighted composite confidence estimate for a composite environment representation; and providing the weighted composite confidence estimate as input for an evaluation of a prediction (130, 132) based on the composite environment representation.