相关论文: Augmented Intelligence in Smart Intersections: Loc…
As autonomous robots increasingly navigate complex and unpredictable environments, ensuring their reliable behavior under uncertainty becomes a critical challenge. This paper introduces a digital twin-based runtime verification for an…
Traffic congestion remains a significant challenge in modern urban networks. Autonomous driving technologies have emerged as a potential solution. Among traffic control methods, reinforcement learning has shown superior performance over…
Autonomous vehicle platforms of varying spatial scales are employed within the research and development spectrum based on space, safety and monetary constraints. However, deploying and validating autonomy algorithms across varying…
Accurate and safety-conscious trajectory prediction is a key technology for intelligent transportation systems, especially in V2X-enabled urban environments with complex multi-agent interactions. In this paper, we created a digital…
Simulators are useful tools for testing automated driving controllers. Vehicle-in-the-loop (ViL) tests and digital twins (DTs) are widely used simulation technologies to facilitate the smooth deployment of controllers to physical vehicles.…
Managing mixed traffic comprising human-driven and robot vehicles (RVs) across large-scale networks presents unique challenges beyond single-intersection control. This paper proposes a reinforcement learning framework for coordinating mixed…
We propose a safe DRL approach for autonomous vehicle (AV) navigation through crowds of pedestrians while making a left turn at an unsignalized intersection. Our method uses two long-short term memory (LSTM) models that are trained to…
Since the traffic administration at road intersections determines the capacity bottleneck of modern transportation systems, intelligent cooperative coordination for connected autonomous vehicles (CAVs) has shown to be an effective solution.…
In this paper, we study a digital twin (DT)-empowered integrated sensing, communication, and computation network. Specifically, the users perform radar sensing and computation offloading on the same spectrum, while unmanned aerial vehicles…
Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, a scheduling agent is associated with each intersection.…
Safe road-crossing by self-driving vehicles is a crucial problem to address in smart-cities. In this paper, we introduce a multi-sensor fusion approach to support road-crossing decisions in a system composed by an autonomous wheelchair and…
As mobile robots increasingly operate alongside humans in shared workspaces, ensuring safe, efficient, and interpretable Human-Robot Interaction (HRI) has become a pressing challenge. While substantial progress has been devoted to human…
Recent advancements in connected autonomous vehicle (CAV) technology have sparked growing research interest in lane-free traffic (LFT). LFT envisions a scenario where all vehicles are CAVs, coordinating their movements without lanes to…
Delayed and partially observable state information poses significant challenges for reinforcement learning (RL)-based control in real-world autonomous driving. In highway on-ramp merging, a roadside unit (RSU) can sense nearby traffic,…
Cooperative autonomous driving plays a pivotal role in improving road capacity and safety within intelligent transportation systems, particularly through the deployment of autonomous vehicles on urban streets. By enabling vehicle-to-vehicle…
Recent advances in sensing technologies, wireless communications, and computing paradigms drive the evolution of vehicles in becoming an intelligent and electronic consumer products. This paper investigates enabling digital twins in…
Digital network twins (DNTs) are virtual representations of physical networks, designed to enable real-time monitoring, simulation, and optimization of network performance. When integrated with machine learning (ML) techniques, particularly…
AI-powered edge computing security is moving Intelligent Transportation Systems (ITS) from passive, rule-based protections to proactive, smart, zero-touch, self-sufficient safeguards that neutralize threats in milliseconds. As…
Vehicle Twins (VTs) as digital representations of vehicles can provide users with immersive experiences in vehicular metaverse applications, e.g., Augmented Reality (AR) navigation and embodied intelligence. VT migration is an effective way…
Human digital twin (HDT) is an emerging paradigm that bridges physical twins (PTs) with powerful virtual twins (VTs) for assisting complex task executions in human-centric services. In this paper, we study a two-timescale online…