Lane Departure Warning Using Ego Motion and Vision Trajectories
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Solution Overview
Problem
Existing lane departure warning systems for vehicles are limited in operation range and accuracy, particularly on roads with high curvature, and often provide false positives due to inadequate consideration of ego vehicle motion and vision information, leading to distracting and unreliable warnings.
Innovation Solution
A vehicle lane departure detection and warning system that combines ego motion and vision data to predict path curvature and rate of curvature, using a weighted combination of low-speed and high-speed parameters based on vehicle speed, to accurately determine time to line crossing and trigger precise warnings, even during high-performance maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lane departure warning systems are used without considering ego motion and vision information, then the system is simpler to implement, but the accuracy and reliability of lane departure detection deteriorates, leading to false positives
Solution Approach 1:
The patent combines multiple information sources including ego motion data from IMU, vision data from cameras, and path prediction parameters to detect lane departure. This integration of multiple sensors and data types improves detection accuracy by providing a more comprehensive view of vehicle state and environment, while resolving the contradiction between simplicity and accuracy through systematic fusion of diverse inputs.
Solution Approach 2:
The patent introduces path prediction parameters as intermediary elements that bridge ego motion information and vision information. These parameters serve as mediators that integrate data from IMU and cameras, enabling accurate lane departure detection by coordinating between different sensor systems and transforming their outputs into meaningful predictive metrics.
2Reliability
If lane departure warning systems do not account for high vehicle speed and high curvature conditions, then the system is easier to operate, but the reliability deteriorates during high-performance maneuvers
Solution Approach 1:
The patent implements dynamic path prediction models that adapt to varying vehicle speeds and road curvatures. The system adjusts its predictions based on real-time ego motion data and vehicle state, enabling reliable lane departure detection during high-performance maneuvers while maintaining operational simplicity through adaptive rather than static processing.
Solution Approach 2:
The patent changes key parameters including path curvature, rate of curvature, and time to line crossing based on vehicle speed and driving conditions. By dynamically adjusting these parameters according to ego motion and vision data, the system maintains high reliability across diverse operating conditions without requiring overly complex fixed-threshold systems.
3Measurement precision
If the system uses weighted combination of low-speed and high-speed path curvature parameters, then the path prediction accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent applies weighted combinations of path curvature parameters selectively based on vehicle speed conditions. Rather than continuously processing all parameters at full complexity, the system adjusts the level of processing according to operational needs, using simplified models when appropriate and more detailed analysis only when required by driving conditions, thus balancing accuracy with computational efficiency.
Data Source
AI summary
An apparatus includes at least one camera configured to capture at least one image of a traffic lane, an inertial measurement unit (IMU) configured to detect motion characteristics, and at least one processor. The at least one processor is configured to obtain a vehicle motion trajectory using the IMU and based on one or more vehicle path prediction parameters, obtain a vehicle vision trajectory based on the at least one image, wherein the vehicle vision trajectory includes at least one lane boundary, determine distances between one or more points on the vehicle and one or more intersection points of the at least one lane boundary based on the obtained vehicle motion trajectory, determine at least one time to line crossing (TTLC) based on the determined distances and a speed of the vehicle, and activate a lane departure warning indicator based on the determined at least one TTLC.