Lane Model Parameter Estimation Using Tangential Direction
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Solution Overview
Problem
Existing lane detection systems face challenges in accurately determining the transition points between road curvature segments, leading to unnatural vehicle control behavior, especially at points like transitions from bends to straight lines, due to high complexity and model errors in filter banks of Kalman filters.
Innovation Solution
A method that generates a lane model using predictive estimation, where the tangential direction of road-parallel structures is used to determine lane model parameters, allowing for precise modeling of road course without filtering quantities or lateral distance dependencies, using edge detection and Hough transforms to identify parallel structures, and employing a Kalman filter or polynomial approximation for estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a filter bank made up of several Kalman filters is used to estimate lane model parameters, then the estimation robustness is improved, but the device complexity and runtime increase significantly
Solution Approach 1:
The patent extracts and uses only the tangential direction information from road-parallel structures, removing the need for complex filter banks. By adopting the tangential direction value directly as the lane model parameter at the corresponding distance, the solution eliminates the computational overhead of multiple Kalman filters while maintaining estimation accuracy.
Solution Approach 2:
The patent copies the tangential direction information from detected road-parallel structures directly into the lane model parameters. This copying approach bypasses the need for complex filtering and estimation processes, achieving simplicity while preserving the essential geometric information needed for accurate lane modeling.
2Stability of the object's composition
If transition points between clothoid segments are filtered out using a filter bank, then estimation stability is improved, but measurement precision of transition points deteriorates
Solution Approach 1:
The patent extracts transition point information directly from the tangential direction changes of road-parallel structures without applying filtering operations. By using the raw tangential direction values at different distances, the method preserves the precise location of transition points while achieving stability through the geometric consistency of parallel structures.
3Measurement precision
If the lane model depends on lateral distance from the middle of the lane, then the model accuracy is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent extracts and uses only the tangential direction information from road-parallel structures, completely removing the dependency on lateral distance measurements. By focusing solely on the angular orientation of parallel structures relative to the vehicle, the solution achieves accurate lane modeling without requiring precise lateral positioning information.
4Reliability
If a filter bank with multiple Kalman filters is implemented, then the reliability of lane model estimation is improved, but the runtime and processing speed decrease
Solution Approach 1:
The patent extracts and directly utilizes tangential direction information from road-parallel structures, eliminating the need for multiple Kalman filter computations. This extraction-based approach maintains estimation reliability by using geometrically consistent information while dramatically reducing computational time and processing requirements.
Solution Approach 2:
The patent copies tangential direction values directly into lane model parameters without iterative filtering processes. This direct copying method preserves the reliability of estimates by maintaining the geometric relationships in the data while achieving real-time processing speeds through the elimination of computationally intensive filter bank operations.
Data Source
AI summary
A method for determining the course of the road for a moving motor vehicle having a surroundings sensor system. Sensor data generated by the surroundings sensor system are evaluated to detect lane-relevant features. A lane model having at least one lane model parameter that determines the course of the lane is generated for the road, structures of at least one distance range that are parallel to the road are detected in the sensor data, the tangential direction of at least the one structure that is parallel to the road is determined, and the value of the tangential direction of the structure that is parallel to the road is adopted as the value of the direction of the tangent line at the point of contact with the structure that is parallel to the road to determine at least the one lane model parameter by predictive estimation in the lane model.