Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network

• URL: http://arxiv.org/abs/2409.18399v1
• Date: Fri, 27 Sep 2024 02:29:02 GMT
• Title: Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network
• Authors: Lei Li, Zhifa Chen, Jian Wang, Bin Zhou, Guizhen Yu, Xiaoxuan Chen,
• Abstract summary: The application of autonomous driving in open-pit mining has garnered increasing attention for achieving safe and efficient transportation. A method is proposed to predict multiple possible trajectories and their probabilities of the target vehicle. The method underwent offline testing on a dataset specifically designed for autonomous driving scenarios in open-pit mining.
• Score: 15.950227451262919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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