Autonomous Vehicle Sensor Parameter Adjustment for Environmental Variations
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Solution Overview
Problem
Autonomous driving vehicles face challenges in maintaining recognition performance due to changes in sunlight and weather conditions, which affect the recognition environment and require dynamic adjustment of recognition algorithm parameters.
Innovation Solution
An external environment recognition apparatus and method that estimates the vehicle's self-location, acquires registration information of known stationary objects, and adjusts specific parameters of the recognition algorithm based on deviations in sensor information to optimize performance in varying environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If recognition algorithm parameters are fixed, then the system is simple to operate, but recognition performance deteriorates under changing environmental conditions
Solution Approach 1:
The recognition algorithm automatically adjusts its own parameters using feedback from recognition results and environmental sensors, without requiring manual intervention. The system monitors recognition performance and autonomously optimizes parameters to maintain high accuracy under varying environmental conditions.
Solution Approach 2:
The system uses recognition results as feedback to dynamically adjust parameters. By analyzing the reliability of recognition outputs and environmental sensor data, the algorithm continuously adapts parameters to optimize performance in real-time across different lighting and weather conditions.
2Reliability
If recognition algorithm parameters are dynamically adjusted according to environment, then recognition performance is improved, but device complexity increases
Solution Approach 1:
The system dynamically changes recognition algorithm parameters based on environmental conditions detected by sensors. Parameters such as detection thresholds, sensitivity levels, and processing priorities are adjusted according to lighting, weather, and traffic conditions to optimize recognition performance.
Solution Approach 2:
The parameter adjustment mechanism serves multiple functions: it optimizes recognition performance, adapts to different environmental conditions, and maintains system reliability across diverse operating scenarios. This multi-functional approach reduces the need for separate optimization systems for different conditions.
3Reliability
If multiple sensor types are used for recognition, then recognition reliability is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts sensor activation and data acquisition frequency based on environmental conditions and recognition requirements. During periods of high reliability needs (e.g., complex traffic scenarios), all sensors are activated. During stable conditions, the system reduces sensor usage to conserve energy while maintaining adequate performance.
Data Source
AI summary
The external recognition apparatus for an autonomous driving vehicle includes an external sensor, at least one processor, and at least one memory communicatively coupled to the at least one processor and storing executable a plurality of instructions. The plurality of instructions is configured to cause the at least one processor to estimate a self-location of an ego-vehicle, acquire registration information of a known stationary object associated with the self-location, acquire sensor information corresponding to the stationary object by the external sensor, and adjust a value of a specific parameter related to the sensor information among parameters of a recognition algorithm based on a deviation between the registration information and the sensor information.