Vehicle Correction Angle Learning Under Road Condition Constraints
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Solution Overview
Problem
Existing methods for determining a vehicle's correction angle for straight-line tracking are inaccurate due to environmental conditions deviating from ideal conditions, such as potholes or cambered road surfaces, leading to incorrect averaging of steering angles.
Innovation Solution
A method and device that learn the correction angle based on vehicle and environment parameters, only allowing learning when specific criteria are met, such as a level, dry, and pothole-free road surface, and evaluate parameters like road type, traffic conditions, and vehicle dynamics to ensure accurate determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the correction angle is calculated as the most frequently used steering angle on average, then the determination process is simple, but environmental conditions like potholes or cambered road surfaces lead to inaccurate correction angle determination
Solution Approach 1:
The patent changes the parameters used for correction angle determination by evaluating multiple environmental parameters (road surface condition, weather conditions, traffic situation, vehicle speed) and only using steering angle data when these parameters indicate favorable conditions. This resolves the contradiction by making the determination process more complex in terms of parameter evaluation but significantly improving the accuracy of the correction angle by excluding data from unfavorable conditions like potholes or cambered roads
2Adaptability or versatility
If learning is performed continuously to adapt to different driving conditions, then the system is highly adaptive, but environmental deviations lead to incorrect learning and inaccurate correction angles
Solution Approach 1:
The patent implements a dynamic learning approach where the system adaptively adjusts its learning behavior based on environmental conditions. Learning is enabled only when environmental parameters indicate favorable conditions (level road, dry surface, no potholes, appropriate weather and traffic). This dynamic enablement/disabling of learning resolves the contradiction by maintaining high adaptability to genuine driving condition changes while preventing incorrect learning from environmental deviations
Solution Approach 2:
The system uses feedback from environmental parameter evaluations to control the learning process. By continuously monitoring road surface conditions, weather, traffic situation, and vehicle speed, the system receives feedback about whether current conditions are suitable for learning. This feedback mechanism ensures that learning only occurs when reliable data can be obtained, resolving the contradiction between adaptability and reliability
Data Source
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AI summary
The invention relates to a device and a method for determining a correction angle (KW) for correcting the straight-ahead travel of a vehicle (1), wherein a learning process (S3) of the correction angle (KW) is carried out as a function of at least one intrinsic vehicle variable (FE), wherein at least one vehicle environment variable (FU) is determined and at least one criterium specific to an environment variable is evaluated, wherein the learning process (S3) is carried out only if the at least one criterion specific to an environment variable is fulfilled.