Real-time crash detection is essential for developing proactive safety management strategy and enhancing overall traffic efficiency. To address the limitations associated with trajectory acquisition and vehicle tracking, road segment maps recording the individual-level traffic dynamic data were directly served in crash detection. A novel two-stage trajectory-free crash detection framework, was present to generate the rational future road segment map and identify crashes. The first-stage diffusion-based segment map generation model, Mapfusion, conducts a noisy-to-normal process that progressively adds noise to the road segment map until the map is corrupted to pure Gaussian noise. The denoising process is guided by sequential embedding components capturing the temporal dynamics of segment map sequences. Furthermore, the generation model is designed to incorporate background context through ControlNet to enhance generation control. Crash detection is achieved by comparing the monitored segment map with the generations from diffusion model in second stage. Trained on non-crash vehicle motion data, Mapfusion successfully generates realistic road segment evolution maps based on learned motion patterns and remains robust across different sampling intervals. Experiments on real-world crashes indicate the effectiveness of the proposed two-stage method in accurately detecting crashes.
University of Washington（华盛顿大学）；University of Tokyo（东京大学）
Traffic near-crash events serve as critical data sources for various smart transportation applications, such as being surrogate safety measures for traffic safety research and corner case data for automated vehicle testing. However, there are several key challenges for near-crash detection. First, extracting near-crashes from original data sources requires significant computing, communication, and storage resources. Also, existing methods lack efficiency and transferability, which bottlenecks prospective large-scale applications. To this end, this paper leverages the power of edge computing to…
In many cases, such as trajectories clustering and classification, we often divide a trajectory into segments as preprocessing. In this paper, we propose a trajectory semantic segmentation method based on learned behavior models. In the proposed method, we learn some behavior models from video sequences. Next, using learned behavior models and a hidden Markov model, we segment a trajectory into semantic segments. Comparing with the Ramer-Douglas-Peucker algorithm, we show the effectiveness of the proposed method.
SegmentDreamer: Towards High-fidelity Text-to-3D Synthesis with Segmented Consistency Trajectory Distillation
Sun Yat-sen University（中山大学）；Great Bay University（大贝大学）；Pazhou Lab (Huangpu)（琶洲实验室）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）
Segment-Level Risk Discovery in Online Handwriting for Alzheimer's Disease Detection
Telecom SudParis, Institut Polytechnique de Paris（巴黎电信学院，巴黎综合理工学院）；School of Computer Science and Information Engineering, Chongqing Technology and Business University（重庆工商大学计算机科学与信息工程学院）
Identifying human actions in complex scenes is widely considered as a challenging research problem due to the unpredictable behaviors and variation of appearances and postures. For extracting variations in motion and postures, trajectories provide meaningful way. However, simple trajectories are normally represented by vector of spatial coordinates. In order to identify human actions, we must exploit structural relationship between different trajectories. In this paper, we propose a method that divides the video into N number of segments and then for each segment we extract trajectories. We th…
FAST: Foreground-aware Diffusion with Accelerated Sampling Trajectory for Segmentation-oriented Anomaly Synthesis
Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China（上海交通大学未来技术全球研究院）；School of Artificial Intelligence, Shenzhen University, Shenzhen, China（深圳大学人工智能学院）；Department of Intelligent Manufacturing, CATL, Ningde, China（CATL智能制造部门）；Department of Computer Science, City University of Hong Kong, Hong Kong, China（香港城市大学计算机科学系）
Automated Wicket-Taking Delivery Segmentation and Trajectory-Based Dismissal-Zone Analysis in Cricket Videos Using OCR-Guided YOLOv8
Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh（拉贾沙希大学计算机科学与工程系，孟加拉国拉贾沙希）；Marshall University, Huntington, WV, USA（马歇尔大学，美国西弗吉尼亚州亨廷顿）
SenseTime Research（商汤科技研究院）；University of Cologne（科隆大学）；EWI gGmbH（EWI能源经济研究所）；The Shanghai AI Laboratory（上海人工智能实验室）
Road network and trajectory representation learning are essential for traffic systems since the learned representation can be directly used in various downstream tasks (e.g., traffic speed inference, and travel time estimation). However, most existing methods only contrast within the same scale, i.e., treating road network and trajectory separately, which ignores valuable inter-relations. In this paper, we aim to propose a unified framework that jointly learns the road network and trajectory representations end-to-end. We design domain-specific augmentations for road-road contrast and trajecto…
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…
Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion
Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工联合研究技术联盟）；Rutgers University（罗格斯大学）；University of Florida（佛罗里达大学）；Massachusetts Institute of Technology（麻省理工学院）
Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
Liquid Argon Time Projection Chambers (LArTPCs) are high resolution particle imaging detectors, employed by accelerator-based neutrino oscillation experiments for high precision physics measurements. While images of particle trajectories are intuitive to analyze for physicists, the development of a high quality, automated data reconstruction chain remains challenging. One of the most critical reconstruction steps is particle clustering: the task of grouping 3D image pixels into different particle instances that share the same particle type. In this paper, we propose the first scalable deep lea…
