Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network
Recently, the application of autonomous driving in open-pit mining has garnered increasing attention for achieving safe and efficient mineral transportation. Compared to urban structured roads, unstructured roads in mining sites have uneven boundaries and lack clearly defined lane markings. This lea...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
26.09.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Recently, the application of autonomous driving in open-pit mining has
garnered increasing attention for achieving safe and efficient mineral
transportation. Compared to urban structured roads, unstructured roads in
mining sites have uneven boundaries and lack clearly defined lane markings.
This leads to a lack of sufficient constraint information for predicting the
trajectories of other human-driven vehicles, resulting in higher uncertainty in
trajectory prediction problems. A method is proposed to predict multiple
possible trajectories and their probabilities of the target vehicle. The
surrounding environment and historical trajectories of the target vehicle are
encoded as a rasterized image, which is used as input to our deep convolutional
network to predict the target vehicle's multiple possible trajectories. The
method underwent offline testing on a dataset specifically designed for
autonomous driving scenarios in open-pit mining and was compared and evaluated
against physics-based method. The open-source code and data are available at
https://github.com/LLsxyc/mine_motion_prediction.git |
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DOI: | 10.48550/arxiv.2409.18399 |