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The Characterized Diffusion Module simulates scenarios with inherent uncertainty and injects semantic confidence cues to improve accuracy. A Spatio-Temporal Interaction Module models scenario effects on vehicle dynamics across spatial and temporal dimensions. Extensive evaluations on NGSIM, HighD, and MoCAD show state-of-the-art performance across short and long horizons in highways, urban streets, and intersections.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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play?",{"text":72,"@type":64},"It captures how traffic scenarios affect vehicle dynamics over both spatial and temporal dimensions, improving the model’s understanding of scenario influence.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},135420,1787311920,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & 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for Good  \nCDSTraj: Characterized Diffusion and Spatial-Temporal Interaction Network for  \nTrajectory Prediction in Autonomous Driving  \nHaicheng Liao 1 , Xuelin Li2 , Yongkang Li2 , Hanlin Kong2 , Chengyue Wang 1 , Bonan Wang 1 , Yanchen Guan 1 , KaHou Tam 1 , Zhenning Li 1  \n1University of Macau  \n2University of Electronic Science and Technology of China  \n{yc27979, chengyuewang, mc35002, yc37976, yc374361, [zhenningli](zhenningli}@um.edu.com)[}](zhenningli}@um.edu.com)[@um.edu.com](zhenningli}@um.edu.com),  \n[lxl.cooper@outlook.com](lxl.cooper@outlook.com), [franklin1234560@163.com](franklin1234560@163.com), [hanlinkong@foxmail.com](hanlinkong@foxmail.com)  \nAbstract  \nTrajectory prediction is a cornerstone in autonomous driving (AD), playing a critical role in enabling vehicles to navigate safely and efficiently in dynamic environments. To address this task, this paper presents a novel trajectory prediction model tailored for accuracy in the face of heterogeneous and uncertain traffic scenarios. At the heart of this model lies the Characterized Diffusion Module, an innovative module designed to simulate traffic scenarios with inherent uncertainty. This module enriches the predictive process by infusing it with detailed semantic information, thereby enhancing trajectory prediction accuracy. Complementing this, our Spatio-Temporal (ST) Interaction Module captures the nuanced effects of traffic scenarios on vehicle dynamics across both spatial and temporal dimensions with remarkable effectiveness. Demonstrated through exhaustive evaluations, our model sets a new standard in trajectory prediction, achieving state-of-the-art (SOTA) results on the Next Generation Simulation (NGSIM), Highway Drone (HighD), and Macao Connected Autonomous Driving (MoCAD) datasets across both short and extended temporal spans. This performance underscores the model’s unparalleled adaptability and efficacy in navigating complex traffic scenarios, including highways, urban streets, and intersections.  \n1 Introduction  \nIn the domain of autonomous driving (AD), trajectory prediction plays a pivotal role by providing invaluable insights for the subsequent trajectory planning module, thereby enhancing the safety of navigation in complex and dynamic traffic scenarios [Huang et al., 2022] . The continuous presence of mixed traffic flow necessitates a trajectory prediction model that deeply understands the heterogeneity and uncertainty of traffic scenarios [Liao et al., 2024e] . Despite the proliferation of trajectory prediction models, significant gaps remain in the thorough investigation of the impact of heterogeneous and uncertain traffic scenarios on future motion.  \nFigure 1: Overview of our model for processing past states. The framework utilizes two specialized modules to accomplish trajectory prediction for the target agent: Characterized Diffusion and Spatial-Temporal Interaction Network. In situations of high uncertainty, characterized diffusion employs a noisy Gaussian function to define a confidence region for the trajectory distribution. Continuous denoising isolates confidence features for future predictions. Meanwhile, the spatial-temporal interaction network extracts features to understand spatial relations and temporal dependency.  \nThe initial gap we pinpoint hinges on the accurate simulation of future traffic scenarios—a cornerstone for enhancing trajectory prediction precision [Wang et al., 2023; Liao et al., 2024d] . The challenge is amplified by the intrinsic uncertainties characterizing traffic dynamics, making the accurate forecast of future scenarios a complex endeavor. Prevailing models have primarily concentrated on uncertainties inherent to the target agent [Zhao et al., 2019; Alahi et al., 2016; Gupta et al., 2018], thereby neglecting the comprehensive uncertainty pervasive in the overall traffic scenarios. T","cbCaiotqtNd0G0Yg","https://ap.wps.com/l/cbCaiotqtNd0G0Yg","pdf",1012486,"English","# Abstract\n# Introduction\n## Background and motivation\n## Identified research gaps\n## Proposed approach and contributions","[{\"question\":\"What problem does CDSTraj address in autonomous driving?\",\"answer\":\"It targets trajectory prediction under heterogeneous and uncertain traffic scenarios, where existing models often fail to comprehensively simulate future uncertainties.\"},{\"question\":\"How does the Characterized Diffusion Module work?\",\"answer\":\"It simulates traffic scenarios using a noisy Gaussian to define a confidence region, then uses continuous denoising to isolate confidence features for future predictions.\"},{\"question\":\"What role does the Spatio-Temporal Interaction Module play?\",\"answer\":\"It captures how traffic scenarios affect vehicle dynamics over both spatial and temporal dimensions, improving the model’s understanding of scenario influence.\"}]","Proceedings of the Thirty-Third International Joint Conference on Arti􀀂cial Intelligence (IJCAI-24) - Special Track on AI for Good - CDSTraj | PDF",23]