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Robust lane sensing and departure warning under shadows and occlusions
Rodolfo Tapia-Espinoza1, Miguel Torres-Torriti
1Department of Electrical Engineering, Pontificia Universidad Cat´olica de Chile, Santiago, Chile. rntapia@puc.cl
Sensors (Basel, Switzerland)
|March 13, 2013
Summary
This study introduces a robust lane detection and tracking method for driver awareness systems, effectively handling shadows and occlusions. The novel approach improves vehicle pose estimation for safer navigation.
Area of Science:
- Computer Vision
- Robotics
- Autonomous Driving Systems
Background:
- Accurate sensing of road geometry and vehicle pose is crucial for driver assistance systems.
- Shadows and occlusions pose significant challenges to traditional lane detection methods.
Purpose of the Study:
- To develop a lane segmentation and tracking approach robust to shadows and occlusions.
- To enhance driver awareness systems with reliable road geometry sensing.
Main Methods:
- Utilized color-based clustering and MSAC (M-Estimator Sample Consensus) for outlier removal and curvature estimation.
- Modeled lane boundaries as 3D planar curves using inverse perspective mapping.
- Employed an extended Kalman filter with a 6-DOF motion model for noise reduction and incorporated road banking/slope data.
Main Results:
- The proposed method demonstrated superior performance compared to traditional gradient-based approaches.
- Successfully achieved robust lane segmentation and tracking under challenging conditions with varying shadows and occlusions.
- Validated the effectiveness of quadratic and cubic approximations for clothoid lane boundary models.
Conclusions:
- The developed approach significantly improves the reliability of lane detection in adverse conditions.
- This method provides a foundation for more advanced driver awareness and autonomous driving systems.
- The 3D modeling of lane boundaries offers enhanced depth perception and pose estimation accuracy.
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