Vehicle Trajectory Prediction

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5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 36

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Collaborative Trajectory Prediction via Late Fusion
Apr 24, 2026Nadya Abdel Madjid, Murad Mebrahtu, Zakhar Yagudin +5Multi-Agent Trajectory Prediction V2X Communication
Khalifa University, Computer Science, Abu Dhabi, UAE · Khalifa University, KUCARS-KU Center for Autonomous Robotic Systems, Department of Computer Science, Abu Dhabi, UAE · Khalifa University, Computer and Information Engineering, Abu Dhabi, UAE +2
Frozen LLMs as Map-Aware Spatio-Temporal Reasoners for Vehicle Trajectory Prediction
Apr 23, 2026Yanjiao Liu, Jiawei Liu, Xun Gong +1Multi-Agent Trajectory Prediction Autonomous Driving
School of Artificial Intelligence, Jilin University, Changchun 130012, PR China.
EdgeVTP: Exploration of Latency-efficient Trajectory Prediction for Edge-based Embedded Vision Applications
Apr 18, 2026Seungjin Kim, Reza Jafarpourmarzouni, Christopher Neff +2Efficient Neural Network Inference Edge Inference
University of Wyoming · University of North Carolina at Charlotte · North Carolina A&T State University
Reverberation: Learning the Latencies Before Forecasting Trajectories
Nov 14, 2025Conghao Wong, Ziqian Zou, Beihao Xia +1Multi-Agent Trajectory Prediction Agent Trajectory Analysis
Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
Fine-Grained Behavior and Lane Constraints Guided Trajectory Prediction Method
Mar 27, 2025Wenyi Xiong, Jian Chen, Ziheng QiVehicle Trajectory Prediction
School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China · Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China · School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China +1
An Empirical Markov Chain Car-Following (MC-CF) Model
Date pendingSungyong Chung, Yanlin Zhang, Nachuan Li +2Intelligent Transportation Systems Vehicle Trajectory Prediction
Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign · Northwestern University Transportation Center, Northwestern University

Apr 24, 2026, cs.ROJ/K move · Enter open · S save

Collaborative Trajectory Prediction via Late Fusion

Nadya Abdel Madjid, Murad Mebrahtu, Zakhar Yagudin, Bilal Hassan+4

Khalifa University, Computer Science, Abu Dhabi, UAE · Khalifa University, KUCARS-KU Center for Autonomous Robotic Systems, Department of Computer Science, Abu Dhabi, UAE · Khalifa University, Computer and Information Engineering, Abu Dhabi, UAE+2

Multi-Agent Trajectory Prediction V2X Communication Vehicle Trajectory Prediction

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Predicting future trajectories of surrounding traffic agents is critical for safe autonomous navigation and collision avoidance. Despite all advances in the trajectory forecasting realm, the prediction models remains vulnerable to uncertainty caused by occlusions, limited sensing range, and perception errors. Collaborative vehicle-to-vehicle (V2V) approaches help reduce this uncertainty by sharing complementary information. Existing collaborative trajectory prediction methods typically fuse feature maps at the perception stage to construct a holistic scene view. Further this holistic representation is decoded into the future trajectories. Such design incurs substantial communication overhead due to the exchange of high-dimensional feature representations and often assumes idealized bandwidth and synchronization, limiting practical deployment. We address these limitations by shifting collaboration from perception to the prediction module and introducing a late-fusion framework for shared forecasts. The framework is model-agnostic and treats collaborating vehicles as independent asynchronous agents. We evaluate the approach on the OPV2V, V2V4Real, and DeepAccident datasets, comparing individual and collaborative forecasting. Across all datasets, late fusion consistently reduces miss rate and improves trajectory success rate (

\mathrm{TSR}_{0.5}

), defined as the fraction of ground-truth agents with final displacement error below 0.5 m. On the real-world V2V4Real dataset, collaborative prediction improves the success rate by

1.69\%

and

1.22\%

for both intelligent vehicles, respectively, compared with individual forecasting.

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Related topics

Latest papers 36

Collaborative Trajectory Prediction via Late Fusion

Frozen LLMs as Map-Aware Spatio-Temporal Reasoners for Vehicle Trajectory Prediction

EdgeVTP: Exploration of Latency-efficient Trajectory Prediction for Edge-based Embedded Vision Applications

Reverberation: Learning the Latencies Before Forecasting Trajectories

Fine-Grained Behavior and Lane Constraints Guided Trajectory Prediction Method

An Empirical Markov Chain Car-Following (MC-CF) Model