Authors:
Sanghyeon Lee
1
;
Donghun Kang
2
and
Min H. Kim
1
Affiliations:
1
School of Computing, KAIST, Daejeon, South Korea
;
2
Autonomous Driving Development Team, Hyundai Motor Company, Seongnam, South Korea
Keyword(s):
Lane Tracking, Autonomous Driving.
Abstract:
Lane recognition and tracking are essential for autonomous driving, providing precise positioning and navigation data for vehicles. Existing single-image lane detection methods often falter in real-world conditions like poor lighting and occlusions. Video-based approaches, while leveraging sequential frames, typically lack continuity in lane tracking, leading to fragmented lane representations. We introduce a novel approach that addresses these challenges through temporally recursive spline modeling, a robust framework designed to maintain consistent, multi-lane tracking over time. Unlike traditional methods that limit tracking to adjacent lanes, our technique models lane trajectories as temporally recursive splines mapped in world space, capturing smooth lane continuity and enhancing long-term tracking fidelity across complex driving scenes. Our framework incorporates 2D image-based lane detections into a recursive spline model, facilitating accurate, real-time lane trajectory repre
sentation across frames. To ensure reliable lane association and continuity, we integrate a Kalman filter and an adaptive Hungarian algorithm, allowing our method to enhance baseline detectors and support consistent multi-lane tracking. Experimental results demonstrate that our temporally recursive spline modeling outperforms conventional approaches in lane detection and tracking metrics, achieving supe-rior continuous lane recognition in challenging driving environments.
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