Vehicle trajectory prediction tasks have been commonly tackled from two distinct perspectives: either with knowledge-driven methods or more recently with data-driven ones. On the one hand, we can explicitly implement domain-knowledge or physical priors such as anticipating that vehicles will follow the middle of the roads. While this perspective leads to feasible outputs, it has limited performance due to the difficulty to hand-craft complex interactions in urban environments. On the other hand, recent works use data-driven approaches which can learn complex interactions from the data leading to superior performance. However, generalization, \textit{i.e.}, having accurate predictions on unseen data, is an issue leading to unrealistic outputs. In this paper, we propose to learn a "Realistic Residual Block" (RRB), which effectively connects these two perspectives. Our RRB takes any off-the-shelf knowledge-driven model and finds the required residuals to add to the knowledge-aware trajectory. Our proposed method outputs realistic predictions by confining the residual range and taking into account its uncertainty. We also constrain our output with Model Predictive Control (MPC) to satisfy kinematic constraints. Using a publicly available dataset, we show that our method outperforms previous works in terms of accuracy and generalization to new scenes. We will release our code and data split here: https://github.com/vita-epfl/RRB.
Rethinking Gaussian Trajectory Predictors: Calibrated Uncertainty for Safe Planning
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校电子与计算机工程系）；Department of Mathematical and Computational Sciences, University of Toronto（多伦多大学数学与计算科学系）
OASIS: Observation-Action Space Alignment via SE(3) Trajectory Prediction for Robotic Manipulation
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）
TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction
Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China（中国科学院系统软件重点实验室（中国科学院）和计算机科学国家重点实验室，软件研究所，中国）；University of Chinese Academy of Sciences, China（中国科学院大学，中国）；Nanjing Institute of Software Technology, Chinese Academy of Sciences, China（中国科学院南京软件技术研究所，中国）；Hangzhou Institute for Advanced Study, UCAS, China（浙江大学杭州高等研究院，中国）
Testing-Driven Reliability Audit of Trajectory-Based Early Outcome Prediction for LLM Agents: Target-Specific Calibration Transfer Persists Within a Single Benchmark
Lizhi College, Shaanxi University of Technology（陕西理工大学砺志学院）
P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving
Tsinghua University（清华大学）；Shanghai Qi Zhi Institute（上海期智研究院）；Massachusetts Institute of Technology（麻省理工学院）
运动预测对于在交互式场景中实现自动驾驶车辆的安全运动规划至关重要。它允许规划者识别与其他交通代理的潜在冲突并生成安全的计划。现有的运动预测器通常专注于减少预测误差，但它们如何帮助规划器识别冲突仍然是一个悬而未决的问题。在本文中，我们通过新的冲突相关的指标，如识别冲突的成功率，评估国家的最先进的预测。令人惊讶的是，预测遭受低成功率，从而导致很大比例的碰撞时，我们在一个交互式模拟器中测试的预测规划系统。为了填补这一空白，我们提出了一个简单但有效的替代方案，它结合了基于物理的轨迹生成器和基于学习的关系预测器来识别冲突并推断冲突关系。我们证明了我们的预测器P4P在Waymo Open Motion Dataset的现实交互式驾驶场景中比现有的基于学习的预测器具有更好的性能。
Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network
Wuhan University of Technology（武汉理工大学）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；University of Waterloo（滑铁卢大学）
MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
School of Mathematical Sciences, Shanghai Jiao Tong University（上海交通大学数学科学学院）；Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University（上海交通大学Bio-X研究院、发育与神经精神疾病遗传学重点实验室）；Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University（上海精神疾病重点实验室、脑科学与技术研究中心，上海交通大学）
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）
Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network
Shanghai Jiao Tong University（上海交通大学）；University of California Irvine（加州大学欧文分校）；University of Technology Sydney（悉尼科技大学）；Australian Artificial Intelligence Institute（澳大利亚人工智能研究所）
Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach
Tsinghua University（清华大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；National University of Singapore（新加坡国立大学）；IDEA Research（IDEA研究院）
