Towards Accurate Ego-lane Identification with Early Time Series Classification
Accurate and timely determination of a vehicle's current lane within a map is a critical task in autonomous driving systems. This paper utilizes an Early Time Series Classification (ETSC) method to achieve precise and rapid ego-lane identification in real-world driving data. The method begins b...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
27.05.2024
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Online Access | Get full text |
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Summary: | Accurate and timely determination of a vehicle's current lane within a map is
a critical task in autonomous driving systems. This paper utilizes an Early
Time Series Classification (ETSC) method to achieve precise and rapid ego-lane
identification in real-world driving data. The method begins by assessing the
similarities between map and lane markings perceived by the vehicle's camera
using measurement model quality metrics. These metrics are then fed into a
selected ETSC method, comprising a probabilistic classifier and a tailored
trigger function, optimized via multi-objective optimization to strike a
balance between early prediction and accuracy. Our solution has been evaluated
on a comprehensive dataset consisting of 114 hours of real-world traffic data,
collected across 5 different countries by our test vehicles. Results show that
by leveraging road lane-marking geometry and lane-marking type derived solely
from a camera, our solution achieves an impressive accuracy of 99.6%, with an
average prediction time of only 0.84 seconds. |
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DOI: | 10.48550/arxiv.2405.17270 |