Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with various open research questions. State-of-the-art approaches regress trajectories either in a one-shot or step-wise manner. Although one-shot approaches are usually preferred for their simplicity, they relinquish powerful self-supervision schemes that can be constructed by chaining multiple time-steps. We address this issue by proposing a middle-ground where multiple trajectory segments are chained together. Our proposed Multi-Branch Self-Supervised Predictor receives additional training on new predictions starting at intermediate future segments. In addition, the model 'imagines' the latent context and 'predicts the past' while combining multi-modal trajectories in a tree-like manner. We deliberately keep aspects such as interaction and environment modeling simplistic and nevertheless achieve competitive results on the INTERACTION dataset. Furthermore, we investigate the sparsely explored uncertainty estimation of deterministic predictors. We find positive correlations between the prediction error and two proposed metrics, which might pave way for determining prediction confidence.
LAD-Drive: Bridging Language and Trajectory with Action-Aware Diffusion Transformers
Institute for Intelligent Systems, Esslingen University of Applied Sciences（智能系统研究所，埃斯林根应用科学大学）；Department of Computer Science, University of Freiburg（计算机科学系，弗赖堡大学）
PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models
Intelligent Transportation Thrust, Systems Hub, and Center of Seamless Connectivity & Connected Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（智能交通 thrust、系统中心及无缝连接与智能连接研究院，香港科学与技术大学（广州））
AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院）；State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与交通国家重点实验室）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Meituan Inc.（美团公司）；Dongfeng Motor Corporation（东风汽车公司）
GCS-Bridging: Restoring Connectivity of Disconnected Convex Sets for Graph-of-Convex-Sets Motion Planning
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology（哈尔滨工业大学机器人技术与系统国家重点实验室）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航天工程学院）
Force Generative Imitation Learning: Bridging Position Trajectory and Force Commands through Control Technique
Graduate School of Systems and Information Engineering, University of Tsukuba（东京大学系统与信息工程研究生院）；Graduate School of Science and Engineering, Saitama University（埼玉大学科学与工程研究生院）；Department of Intelligent Interaction Technologies, Institute of Systems and Information Engineering, University of Tsukuba（东京大学系统与信息工程研究所智能交互技术系）
WorldMAP: Bootstrapping Vision-Language Navigation Trajectory Prediction with Generative World Models
Harbin Institute of Technology（哈尔滨工业大学）；Shandong University（山东大学）；Tsinghua University（清华大学）；University of Science and Technology of China（中国科学技术大学）；Zhongguancun Academy（中关村学院）
Bridging Perception and Planning: Towards End-to-End Planning for Signal Temporal Logic Tasks
School of Automation & Intelligent Sensing, Shanghai Jiao Tong University（自动化与智能感知学院，上海交通大学）；University of Minnesota, Twin Cities（明尼苏达大学，双城分校）；School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）
