Lane Estimation Confidence Modifies Vehicle Behavior
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately estimating lane boundaries and adjusting their driving behavior based on the confidence level of these estimates, which can impact safety and efficiency, especially in environments with unclear or changing road conditions.
Innovation Solution
A computing device is configured to receive lane information from sensors, estimate lane boundaries, determine the confidence level of these estimates, and modify the vehicle's driving behavior accordingly, allowing for adherence to the lane boundaries based on the confidence level, thereby controlling the vehicle in autonomous or semi-autonomous modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the vehicle strictly adheres to estimated lane boundaries, then lane following accuracy is improved, but safety is compromised when lane estimation confidence is low
Solution Approach 1:
The system dynamically adjusts the degree of adherence to lane boundaries based on the confidence level of lane estimation. When confidence is high, the vehicle strictly follows the lane boundary; when confidence is low, the vehicle reduces adherence and may disregard the estimated boundary. This dynamic adjustment resolves the contradiction by making lane following accuracy subordinate to safety when estimation reliability is questionable.
Solution Approach 2:
The system changes the parameter of lane adherence degree based on the confidence parameter of lane estimation. By modifying the adherence parameter according to confidence levels, the system achieves high lane following accuracy when appropriate while maintaining safety as the priority when estimation quality is uncertain.
2Reliability
If the vehicle adjusts driving behavior based on confidence levels, then safety is improved, but driving efficiency deteriorates due to frequent behavior changes
Solution Approach 1:
The system employs dynamic driving behaviors that adapt to changing confidence levels in real-time. This allows the vehicle to optimize safety by responding to estimation quality while maintaining efficiency through smooth, context-appropriate behavior adjustments rather than rigid, inefficient responses.
Solution Approach 2:
The system changes driving behavior parameters based on confidence level parameters. By adjusting behavior dynamically according to estimation quality, the system improves safety without causing excessive efficiency loss, as the behavior changes are proportional and context-appropriate rather than arbitrary.
3Measurement precision
If the vehicle maintains strict lane adherence, then lane estimation accuracy is improved, but adaptability to changing road conditions deteriorates
Solution Approach 1:
The system dynamically adjusts lane adherence based on confidence in lane estimation, which itself is derived from road condition analysis. This dynamic approach allows the system to maintain high lane following accuracy when conditions are good while adapting to changing road conditions when confidence decreases, thus resolving the contradiction between accuracy and adaptability.
Solution Approach 2:
The system changes the lane adherence parameter based on confidence parameter, which reflects road condition quality. This parameter adjustment mechanism enables the system to achieve accurate lane following when appropriate while maintaining adaptability to varying road conditions through proportional response adjustments.
Data Source
AI summary
Methods and systems for modifying vehicle behavior based on confidence in lane estimation are described. In an example, a computing device may be configured to receive lane information relating to locations of lane boundaries and may be configured to estimate a lane boundary on a road on which the vehicle is traveling, based on the lane information. The computing device also may be configured to determine a level of confidence for the estimated lane boundary, modify a driving behavior for the vehicle based on the level of confidence, and also may be configured to control the vehicle based on the modified driving behavior.