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
Stochastic Trajectory Prediction under Unstructured Constraints
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Macau University of Science and Technology（澳门科技大学）
Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Economics and Management, Beihang University（北京航空航天大学经济管理学院）；Shenzhen Institute of Beihang University（北京航空航天大学深圳研究院）
Trajectory-Level Automatic Curriculum Learning for Legged Locomotion on Unstructured Terrain
National Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室（BIGAI））；Beijing Normal University（北京师范大学）；Communication University of China（中国传媒大学）；Shanghai Jiao Tong University（上海交通大学）
Trajectory Data Mining and Trip Travel Time Prediction on Specific Roads
Data Science Consultant（数据科学顾问）；Datamatics Technologies（Datamatics技术公司）；Department of CS&IT Women University of AJ&K（CS&IT系妇女大学AJ&K分校）；Classified institute（保密机构）；Department of Computer Science（计算机科学系）；Kohsar University（Kohsar大学）；Disruptive Innovation Laboratory（颠覆性创新实验室）；PIEAS
Soochow University（苏州大学）；Hong Kong University of Science and Technology（香港科技大学）；Tencent Inc.（腾讯公司）；ByteDance Inc.（字节跳动公司）；Zhejiang University（浙江大学）；Zhejiang Normal University（浙江师范大学）
ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model
Southern University of Science and Technology（南方科技大学）；City University of Hong Kong（香港城市大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；York University（约克大学）
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving
FZI Research Center for Information Technology（FZI信息技术研究中心）；University of Kassel（卡塞尔大学）；University of Wuppertal（伍珀塔尔大学）；German Aerospace Center (DLR)（德国航空航天中心（DLR））；Robert Bosch GmbH（罗伯特·博世有限公司）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics
School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；School of Computing Technologies, RMIT University（RMIT大学计算技术学院）
Learning Spatio-Temporal Dynamics for Trajectory Recovery via Time-Aware Transformer
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Shanghai Key Laboratory of Data Science, Fudan University（复旦大学数据科学重点实验室）；Shanghai Institute of Intelligent Electronics & Systems（上海智能电子与系统研究所）；School of Computing and Information Systems, Singapore Management University（新加坡国立大学计算与信息系统学院）
MSRFormer: Road Network Representation Learning using Multi-scale Feature Fusion of Heterogeneous Spatial Interactions
Information Engineering University（信息工程大学）；Fujian Normal University（福建师范大学）；Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences（中国科学院海西研究院泉州装备制造研究所）；Norwegian University of Science and Technology（挪威科技大学）；China Centre for Resources Satellite Data and Application（中国资源卫星应用中心）；Henan Twenty First Century Aerospace Technology Co, Ltd.（河南二十一世纪航天技术有限公司）；Shandong University of Science and Technology（山东科技大学）
