A commonly-used representation for motion prediction of actors is a sequence of waypoints (comprising positions and orientations) for each actor at discrete future time-points. While this approach is simple and flexible, it can exhibit unrealistic higher-order derivatives (such as acceleration) and approximation errors at intermediate time steps. To address this issue we propose a simple and general representation for temporally continuous probabilistic trajectory prediction that is based on polynomial trajectory parameterization. We evaluate the proposed representation on supervised trajectory prediction tasks using two large self-driving data sets. The results show realistic higher-order derivatives and better accuracy at interpolated time-points, as well as the benefits of the inferred noise distributions over the trajectories. Extensive experimental studies based on existing state-of-the-art models demonstrate the effectiveness of the proposed approach relative to other representations in predicting the future motions of vehicle, bicyclist, and pedestrian traffic actors.
Horizon Robotics；University of Hong Kong（香港大学）；University of the Chinese Academy of Sciences（中国科学院大学）；Nanjing University（南京大学）；Beijing Jiaotong University（北京交通大学）
Leibniz University Hannover（汉诺威莱布尼茨大学）；PhiGent Robotics（鉴智机器人）；University of Twente（特文特大学）；VISCODA GmbH（VISCODA有限公司）
Trajectory prediction for autonomous driving must continuously reason the motion stochasticity of road agents and comply with scene constraints. Existing methods typically rely on one-stage trajectory prediction models, which condition future trajectories on observed trajectories combined with fused scene information. However, they often struggle with complex scene constraints, such as those encountered at intersections. To this end, we present a novel method, called LAformer. It uses a temporally dense lane-aware estimation module to select only the top highly potential lane segments in an HD…
Pedestrian trajectory prediction is an essential and challenging task for a variety of real-life applications such as autonomous driving and robotic motion planning. Besides generating a single future path, predicting multiple plausible future paths is becoming popular in some recent work on trajectory prediction. However, existing methods typically emphasize spatial interactions between pedestrians and surrounding areas but ignore the smoothness and temporal consistency of predictions. Our model aims to forecast multiple paths based on a historical trajectory by modeling multi-scale graph-bas…
New York University（纽约大学）；University of Chinese Academy of Sciences（中国科学院大学）；Alibaba Group（阿里巴巴集团）
LiDAR point clouds collected from a moving vehicle are functions of its trajectories, because the sensor motion needs to be compensated to avoid distortions. When autonomous vehicles are sending LiDAR point clouds to deep networks for perception and planning, could the motion compensation consequently become a wide-open backdoor in those networks, due to both the adversarial vulnerability of deep learning and GPS-based vehicle trajectory estimation that is susceptible to wireless spoofing? We demonstrate such possibilities for the first time: instead of directly attacking point cloud coordinat…
Middle East Technical University（中东技术大学）；KUIS AI Center（KUIS人工智能中心）；Koç University（科奇大学）
Predicting future locations of agents in the scene is an important problem in self-driving. In recent years, there has been a significant progress in representing the scene and the agents in it. The interactions of agents with the scene and with each other are typically modeled with a Graph Neural Network. However, the graph structure is mostly static and fails to represent the temporal changes in highly dynamic scenes. In this work, we propose a temporal graph representation to better capture the dynamics in traffic scenes. We complement our representation with two types of memory modules; on…
MoE-Enhanced Multi-Domain Feature Selection and Fusion for Fast Map-Free Trajectory Prediction
School of Mechanical Engineering, Zhejiang University（浙江大学机械工程学院）；Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology（南方科技大学自动化与智能制造学院）；Leapmotor Technology（Leapmotor科技）；Department of Aeronautical and Automotive Engineering, Loughborough University（伦敦大学洛桑学院航空与汽车工程系）
Improving Robotic Imitation Learning via Trajectory Standardization
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；D-Robotics
In this paper, we propose a novel trajectory learning method that exploits motion trajectories on topological map using recurrent neural network for temporally consistent geolocalization of object. Inspired by human's ability to both be aware of distance and direction of self-motion in navigation, our trajectory learning method learns a pattern representation of trajectories encoded as a sequence of distances and turning angles to assist self-localization. We pose the learning process as a conditional sequence prediction problem in which each output locates the object on a traversable path in …
LiDAR-BIND-T: Improved and Temporally Consistent Sensor Modality Translation and Fusion for Robotic Applications
IDLab - Faculty of Applied Engineering, University of Antwerp - imec（IDLab - 应用工程学院，安特卫普大学 - imec）；Cosys-Lab - Faculty of Applied Engineering, University of Antwerp（Cosys-Lab - 应用工程学院，安特卫普大学）；Flanders Make Strategic Research Centre（弗拉芒制造战略研究中心）
本文提出 LiDAR-BIND-T，通过时间嵌入相似度、运动对齐损失和窗口化时间融合增强异构传感器到 LiDAR 的转换，并以新指标验证其提升 SLAM 稳定性与精度。
Video anomaly detection is a core problem in vision. Correctly detecting and identifying anomalous behaviors in pedestrians from video data will enable safety-critical applications such as surveillance, activity monitoring, and human-robot interaction. In this paper, we propose to leverage trajectory localization and prediction for unsupervised pedestrian anomaly event detection. Different than previous reconstruction-based approaches, our proposed framework rely on the prediction errors of normal and abnormal pedestrian trajectories to detect anomalies spatially and temporally. We present exp…
VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale
Institute of Artificial Intelligence, University of Central Florida（人工智能研究所，中央佛罗里达大学）；Luleå Tekniska Universitet（卢勒奥理工大学）；Amazon Prime Video Science（亚马逊Prime视频科学）
We focus on the task of estimating a physically plausible articulated human motion from monocular video. Existing approaches that do not consider physics often produce temporally inconsistent output with motion artifacts, while state-of-the-art physics-based approaches have either been shown to work only in controlled laboratory conditions or consider simplified body-ground contact limited to feet. This paper explores how these shortcomings can be addressed by directly incorporating a fully-featured physics engine into the pose estimation process. Given an uncontrolled, real-world scene as inp…
Diffusion^2: Dual Diffusion Model with Uncertainty-Aware Adaptive Noise for Momentary Trajectory Prediction
Civil and Environmental Engineering Department, University of Wisconsin-Madison（威斯康星大学麦迪逊分校土木与环境工程系）；Civil and Environmental Engineering Department, University of Washington（华盛顿大学土木与环境工程系）；College of Transportation, Tongji University（同济大学交通运输学院）
DyG$^2$T: Modeling Object Dynamics with 3D Gaussian Temporal-Spatial Particle Graph Transformer
School of Computer Science and Technology, Harbin Institute of Technology at Weihai（哈尔滨工业大学（威海）计算机科学与技术学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）
Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network. Recurrent neural networks and their variants have been reported in recent research to predict vehicle mobility. However, the spatial attribute of vehicle movement behavior has been overlooked, resulting in incomplete information utilization. To bridge this gap, we put forward for th…
We propose a robust solution to future trajectory forecast, which can be practically applicable to autonomous agents in highly crowded environments. For this, three aspects are particularly addressed in this paper. First, we use composite fields to predict future locations of all road agents in a single-shot, which results in a constant time complexity, regardless of the number of agents in the scene. Second, interactions between agents are modeled as a non-local response, enabling spatial relationships between different locations to be captured temporally as well (i.e., in spatio-temporal int…
CylindTrack: Depth-Aware Cylindrical Motion Modeling for Panoramic Multi-Object Tracking
School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China（人工智能与机器人学院及机器人视觉感知与控制技术国家工程研究中心，湖南大学，中国）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University, China（先进技术学院，西安交通大学利物浦大学，中国）；Suzhou VSDeep Intelligent Technology Co., Ltd., China（苏州VSDeep智能科技有限公司，中国）；BWTON Technology Co., Ltd., China（BWTON科技有限公司，中国）
