用图像编码环境与轨迹，预测矿区无车道线场景下的多条行车路径。
Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network
- 将环境和历史轨迹转为栅格图输入卷积网络
- 在矿区数据集上实现多路径概率预测
- 适合无人矿车、复杂非结构化道路场景
近年来，自动驾驶在露天矿场的应用日益受到关注，有助于实现安全高效的矿石运输。与城市结构化道路相比，矿区的非结构化道路边界不规则且缺乏清晰车道线，导致对其他人工驾驶车辆轨迹的约束信息不足，使轨迹预测不确定性更高。本文提出一种方法，用于预测目标车辆的多条可能轨迹及其概率。将周围环境及目标车辆的历史轨迹编码为栅格图像，并作为深度卷积网络的输入，以预测目标车辆的多条可能轨迹。该方法在专为露天矿自动驾驶场景设计的数据集上进行了离线测试，并与基于物理的方法进行了对比评估。开源代码与数据已发布于 https://github.com/LLsxyc/mine_motion_prediction.git。
原文摘要 · Abstract (English)
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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