With the rapid development of autonomous driving technology, multi-agent trajectory prediction has become the core foundation of autonomous driving algorithms. Efficiently and accurately predicting the future trajectories of multiple agents is key to...
Multi-agent trajectory prediction is essential for autonomous driving systems, as its performance heavily depends on the quality of feature representations. This paper proposes PFR-HiVT, a lightweight and effective approach for multi-agent trajectory...
Accurate and efficient multi-agent trajectory prediction remains a core challenge for autonomous driving, particularly in modeling complex interactions while maintaining physical plausibility and computational efficiency. Many existing methods- espec...
Trajectory prediction is a critical component of autonomous driving, intelligent transportation systems, and human–robot interactions, particularly in complex environments like intersections, where diverse road constraints and multi-agent inter...
Accurately predicting the long-term trajectories of agents in complex traffic environments is crucial for the safety and effectiveness of autonomous driving systems. This paper introduces DBN-MACTraj, a probabilistic model that takes historical traje...
The accurate prediction of the future trajectories of traffic participants is crucial for enhancing the safety and decision-making capabilities of autonomous vehicles. Modeling social interactions among agents and revealing the inherent relationships...
Trajectory prediction is a critical component of autonomous driving decision-making systems, directly impacting driving safety and traffic efficiency. Despite advancements, existing reviews exhibit limitations in timeliness, classification frameworks...
The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integr...
In this paper, a delay-informed multi-agent twin delayed deep deterministic policy gradient (DI-MATD3) approach is proposed for multi-UAV formation trajectory planning subject to communication delays. Specifically, a timestamped per-link communicatio...
Trajectory prediction in urban scenarios is critical for high-level automated driving systems. However, this task is associated with many challenges. On the one hand, a scene typically includes different traffic participants, such as vehicles, buses,...
The continuous change in formation configurations offers significant potential for formation flight. However, existing methods neglect the potential of autonomous navigation and learning ability based on environmental conditions, which would limit sc...
Trajectory prediction is essential for the safe driving of autonomous vehicles. With the advancement of advanced sensors and deep learning technologies, attempts have been made to reflect complex interactions. In this study, we propose a deep learnin...
End-to-end autonomous driving has demonstrated remarkable potential due to its strong scene-understanding capabilities. However, its performance degrades significantly in the presence of occlusions and complex multi-agent interactions, posing serious...
This paper studies the plan recognition problem of multi-agent systems with temporal logic tasks. The high-level temporal tasks are represented as linear temporal logic (LTL). We present a probabilistic plan recognition algorithm to predict the futur...
The development of sensor technology and deep learning has significantly improved the reliability and practicality of automatic driving technology. In an autonomous driving system, agent trajectory prediction is a complex challenge, which includes th...
Accurate and reliable trajectory prediction is crucial for autonomous vehicles to achieve safe and efficient operation. Vehicles perceive the historical trajectories of moving objects and make predictions of behavioral intentions for a future period...
Flocking control for multi-agent systems exhibits promising application prospects in numerous areas including cooperative target search, environmental monitoring, disaster rescue, and intelligent transportation. To solve the problem of collision-avoi...
In modern naval confrontation systems, adversarial underwater unmanned vehicles (UUVs) pose significant challenges, which are deployed on unmanned aerial vehicles (UAVs) due to their inherent mobility and positional uncertainty. Effective neutralizat...
In this study, a hierarchical chassis control strategy is designed to enhance vehicle economy and safety for four-wheel independent-drive electric vehicles (FWID-EVs). In the upper-level, a vehicle dynamics model based on multiple agents is proposed,...
The efficient coordination of multi-Unmanned Aerial Vehicle (UAV) systems in the increasingly complex domain of aerial tasks is hampered by significant challenges, including partial observability, low sample efficiency, and difficulties in inter-agen...
Autonomous cleaning robots operating in semi-structured dynamic environments must execute task-oriented motions while safely interacting with surrounding agents. These agents include pedestrians, vehicles, and other robots. In such environments (e.g....
This paper considers trajectory a modeling problem for a multi-agent system by using the Gaussian processes. The Gaussian process, as the typical data-driven method, is well suited to characterize the model uncertainties and perturbations in a comple...
The heterogeneity of human driving styles in mixed-traffic environments manifests as divergent decision-making behaviors in complex scenarios like highway merging. By accurately recognizing these driving styles and predicting corresponding trajectori...
Despite rapid advances in vehicle intelligence and connectivity, there is still a significant period in mixed traffic where connected, automated vehicles and human-driven vehicles coexist. The behavioral uncertainty of human-driven vehicles makes dec...
Coordinating robotic teams across multiple concurrent search tasks is a critical challenge in search and rescue operations. This work presents a new multi-agent framework designed to manage and optimize search efforts when several missing-person repo...
Predicting target intent in complex, dynamic multi-agent environments remains a formidable challenge due to incomplete perception and the highly dynamic nature of multi-target interactions. Conventional approaches—such as D-S evidence theory, e...
Multi-agent trajectory prediction serves as a critical component in autonomous driving systems, bridging environment perception, behavior understanding, and motion planning. Its outputs not only affect candidate trajectory evaluation and interactive...
Traditional multi-agent reinforcement learning (MARL) algorithms typically implement global parameter sharing across various types of heterogeneous agents without meticulously differentiating between different action semantics. This approach results...
Cooperative multi-UAV pursuit–evasion under occlusions and sensor noise is challenged by intermittent observability of the evader, varying observation-window lengths, and non-stationary evader tactics, all of which destabilize prediction and un...
Autonomous vehicles (AVs) are expected to operate safely and efficiently in complex urban environments characterized by dynamic and uncertain elements such as pedestrians, cyclists and adverse weather. Although current neural network-based decision-m...
This article presents an interaction-aware motion planning framework that integrates a graph neural network (GNN) based multi-modal trajectory predictor with a model predictive control (MPC) based planner. Unlike past studies that predict a single fu...
Grid-based multi-agent path finding (MAPF) solvers scale to large teams, but their discrete schedules may not provide high-quality continuous finite-radius motions near the square-grid corner-passing threshold. We study endpoint-time-preserving geome...
Rapid search for evasive targets using multiple Unmanned Aerial Vehicles (UAVs) presents significant challenges, as it requires real-time target-motion prediction, multi-agent coordination, and adherence to kinematic constraints. Existing cooperative...
Large Language Model (LLM)-driven social simulation offers a controllable approach for analyzing crisis responses, but existing work on online crises often emphasizes either user engagement prediction or opinion and eWOM evolution. This separation is...
In recent years, multi-UAV systems have demonstrated broad applications in both security and civilian domains, where cooperative encirclement has emerged as a key research focus. However, existing work predominantly addresses single-target scenarios...
The widespread adoption of autonomous vehicle technologies requires accurate predictions of coordinated multi-agent motion. While predicting such motion under arbitrary control mechanisms is generally intractable, this paper focuses on certain classe...
To address the issues of slow response to preceding vehicles and poor string stability in distributed platoon control of connected and autonomous vehicles (CAVs) under mixed traffic flow, this paper proposes a sliding mode control method based on LST...
This study explores an innovative approach to integrating modern technology with traditional culture by replacing human performers with clusters of intelligent unmanned vehicles in the Chinese Lunar New Year bench dragon dance. The primary technical...
Predicting the future trajectories of multiple agents is essential for various applications in real life, such as surveillance systems, autonomous driving, and social robots. The trajectory prediction task is influenced by many factors, including ind...
Traffic state estimation (TSE) is a core task in intelligent transportation systems (ITSs) that seeks to infer key operational parameters—such as speed, flow, and density—from limited observational data. Existing methods often face challe...
Coordinated ramp metering (CRM) is an effective strategy for mitigating congestion on urban expressways. It utilizes traffic flow measurements from the expressway network to regulate multi-ramp signals for optimal system performance. Many CRM approac...
The implementation of safe and reliable Autonomous Driving Stacks in complex urban environments remains a formidable engineering challenge. While classical modular pipelines provide necessary component-level interpretability, they are inherently rigi...
Agentic artificial intelligence requires safeguards that remain effective across trajectories rather than only at individual decisions. This study introduces MARIS-TRA, a hierarchical temporal runtime assurance extension of Controlled Agentic AI Syst...
Forecasting the trajectory of pedestrians in shared urban traffic environments from non-invasive sensor modalities is still considered one of the challenging problems facing the development of autonomous vehicles (AVs). In the literature, this proble...
Reinforcement learning (RL) is increasingly used for real-time control of complex dynamical systems, but its practical performance must be evaluated under hardware constraints that are often simplified in simulation. This paper presents a comprehensi...
Artificial intelligence systems deployed in safety-critical and regulated environments require not only predictive performance, but also strict adherence to operational constraints, auditability, and reproducibility. However, in most contemporary arc...
Pedestrian trajectory prediction remains a challenging task, particularly in long-tail scenarios where goal distributions are sparse and inter-agent behaviors are uncertain. In this work, we propose to disentangle the trajectory prediction task into...
The development of complex equipment faces prominent challenges, including unequal status among participating agents, multi-objective full confrontation, strong super-conflict among multi-indicators, dynamic risk evolution, delayed on-site perception...
Pedestrian trajectory prediction from a vehicle-mounted perspective is essential for autonomous driving in complex urban environments yet remains challenging due to ego-motion jitter, frequent occlusions, and scene variability. Existing approaches, l...
Trajectory prediction under multimodal information is critical for autonomous driving, necessitating the integration of dynamic vehicle states and static high-definition (HD) maps to model complex agent–scene interactions effectively. However,...
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