KiGRAS: Kinematic-Driven Generative Model for Realistic Agent Simulation
University of Science and Technology of China（中国科学技术大学）；Mach Drive（马赫动力）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Jianbo Zhao, Jiaheng Zhuang, Qibin Zhou, Taiyu Ban, Ziyao Xu, Hangning Zhou, Junhe Wang, Guoan Wang, Zhiheng Li, Bin Li
Trajectory generation is a pivotal task in autonomous driving. Recent studies have introduced the autoregressive paradigm, leveraging the state transition model to approximate future trajectory distributions. This paradigm closely mirrors the real-world trajectory generation process and has achieved notable success. However, its potential is limited by the ineffective representation of realistic trajectories within the redundant state space. To address this limitation, we propose the Kinematic-Driven Generative Model for Realistic Agent Simulation (KiGRAS). Instead of modeling in the state space, KiGRAS factorizes the driving scene into action probability distributions at each time step, providing a compact space to represent realistic driving patterns. By establishing physical causality from actions (cause) to trajectories (effect) through the kinematic model, KiGRAS eliminates massive redundant trajectories. All states derived from actions in the cause space are constrained to be physically feasible. Furthermore, redundant trajectories representing identical action sequences are mapped to the same representation, reflecting their underlying actions. This approach significantly reduces task complexity and ensures physical feasibility. KiGRAS achieves state-of-the-art performance in Waymo's SimAgents Challenge, ranking first on the WOMD leaderboard with significantly fewer parameters than other models. The video documentation is available at \url{https://kigras-mach.github.io/KiGRAS/}.
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）
EA-WM: Event-Aware Generative World Model with Structured Kinematic-to-Visual Action Fields
Fudan University（复旦大学）；Zhongguancun Academy（中关村学院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；University of Science and Technology of China（中国科学技术大学）；DeepCybo（深瞳）
DRIFT: Diffusion-based Rule-Inferred For Trajectories
School of Management, Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei University of Technology（管理学院、教育部智能决策与信息系统技术工程研究中心、合肥工业大学）
Sensor-Space Based Robust Kinematic Control of Redundant Soft Manipulator by Learning
Department of Mechanical and Aerospace Engineering, The University of Manchester, Manchester, United Kingdom（机械与航空航天工程系，曼彻斯特大学，英国曼彻斯特）；Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, United Kingdom（电气与电子工程系，曼彻斯特大学，英国曼彻斯特）
SAGE: A Socially-Aware Generative Engine for Heterogeneous Multi-Agent Navigation
Guohao School, Tongji University（同济大学国豪书院）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能无人系统科学中心）；Tongji University（同济大学）；Western University（西安大略大学）；Aeromind Technology Co., Ltd.（深圳智元科技有限公司）；University at Buffalo-SUNY（纽约州立大学布法罗分校）
MIMIC-MJX: Neuromechanical Emulation of Animal Behavior
Department of Organismic and Evolutionary Biology（有机与进化生物学系）；Harvard University（哈佛大学）；Computational Neurobiology Laboratory（计算神经生物学实验室）；Salk Institute for Biological Studies（生物研究 institute）；Neurosciences Graduate Program（神经科学研究生项目）；University of California San Diego（加州大学圣地亚哥分校）；Mila；School of Computer Science（计算机科学学院）；McGill University（麦吉尔大学）；University of Washington（华盛顿大学）；eScience Institute（eScience 院）；Computational Neuroscience Center（计算神经科学中心）；Department of Brain and Cognitive Sciences（脑与认知科学系）；Massachusetts Institute of Technology（麻省理工学院）；Picower Institute for Learning and Memory（记忆学习研究所）；Molecular Neurobiology Laboratory（分子神经生物学实验室）；Department of Bioengineering（生物工程系）；Imperial College London（帝国理工学院）；Howard Hughes Medical Institute（霍华德·休斯医学研究所）
