Travel Environment Recognition Using IMU-Corrected Road Gradient
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Solution Overview
Problem
Existing travel environment recognition systems require complex calculation and image processing to estimate three-dimensional structures of road boundaries, leading to increased processing loads and potential inaccuracies in gradient recognition, which can hinder accurate lane keeping and self-driving capabilities.
Innovation Solution
A vehicle control apparatus that utilizes an imaging unit, inertial measurement unit (IMU), and a microprocessor to detect external situations and vehicle states, correcting road gradient recognition based on IMU data to reduce processing complexity and improve accuracy, using a gradient value correction unit to adjust road gradient values and division line recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional structure estimation is performed based on captured images, then traveling path recognition capability is improved, but processing load increases
Solution Approach 1:
The patent introduces an intermediary correction mechanism that uses IMU detection values as a mediator to adjust the recognition results. Instead of performing complex three-dimensional structure estimation directly from images, the system uses the IMU data as an intermediate reference to correct the gradient recognition, thereby reducing processing complexity while maintaining accuracy.
Solution Approach 2:
The patent replaces the complex image processing and three-dimensional structure estimation mechanism with a simpler correction mechanism based on IMU detection. By substituting the mechanical/image processing system with an inertial measurement-based correction system, the processing load is significantly reduced while preserving the essential functionality of accurate path recognition.
2Measurement precision
If complex image processing is used for boundary line estimation, then environment recognition capability is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary correction by using IMU detection values to adjust the recognition results before final path planning. This preliminary action based on inertial data allows the system to quickly compensate for gradient recognition errors without waiting for time-consuming image processing to complete, thereby reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The patent substitutes time-consuming complex image processing with a faster inertial measurement-based correction system. The IMU-based correction mechanism processes data much faster than traditional image processing pipelines, significantly reducing processing time while maintaining or improving recognition accuracy through direct physical measurement of vehicle state.
3Device complexity
If gradient change is not properly corrected, then processing simplicity is maintained, but recognition accuracy deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where IMU detection values are continuously used to correct recognition results. The system feeds back the vehicle state information from the IMU to adjust and refine the gradient recognition, ensuring high accuracy is maintained even on roads with gradient changes, while keeping the processing mechanism relatively simple through this closed-loop correction approach.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately recognizes travel environments and lane boundaries with reduced processing load, enabling precise self-driving even on gradient-changing roads, enhancing safety and efficiency.
Implementation Method 1
an inertial measurement unit (IMU) that detects an attitude angle of the subject vehicle
Data Source
AI summary
A travel environment recognition apparatus includes: an external situation detection unit configured to detect an external situation around a subject vehicle; a state detection unit configured to detect a state of the subject vehicle; a microprocessor and a memory coupled to the microprocessor. The microprocessor is configured to perform: recognizing a travel environment in front of the subject vehicle based on a detection value of the external situation detection unit; and correcting a recognition result of the environment recognition unit based on the detection value of the state detection unit.