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Found 24 papers, 3 papers with code
On Joint Convergence of Traffic State and Weight Vector in Learning-Based Dynamic Routing with Value Function Approximation
Learning-based approaches are increasingly popular for traffic control problems.
Minimax Least-Square Policy Iteration for Cost-Aware Defense of Traffic Routing against Unknown Threats
We develop an least-square policy iteration algorithm for the defender to compute a cost-aware and threat-adaptive defensive strategy.
V2X-Real: a Largs-Scale Dataset for Vehicle-to-Everything Cooperative Perception
Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the perception capability.
Thinking Like an Expert:Multimodal Hypergraph-of-Thought (HoT) Reasoning to boost Foundation Modals
Inspired by this, this paper innovatively proposes a multimodal Hypergraph-of-Thought (HoT) reasoning paradigm, which enables the foundation models to possess the expert-level ability of high-order multi-hop reasoning and multimodal comparative judgement.
An Approximate Dynamic Programming Approach to Vehicle Platooning Coordination in Networks
To simplify the problem, we decouple the action space by prioritizing routing decisions based on travel time estimation.
Physics-informed Machine Learning for Calibrating Macroscopic Traffic Flow Models
Our approach informs the decoder of the physical traffic flow models and thus induces the encoder to yield reasonable traffic parameters given flow and speed measurements.
Robust Queue Length Estimation for Ramp Metering in a Connected Vehicle Environment
Connected vehicles (CVs) can provide numerous new data via vehicle-to-vehicle or vehicle-to-infrastructure communication.
How Does Driver Non-compliance Destroy Traffic Routing Control?
For links admiting congestion propagation, we present one stability condition and one instability condition.
Vehicle Sequencing at Signal-Free Intersections: Analytical Performance Guarantees Based on PDMP Formulation
Signal-free intersections are a representative application of smart and connected vehicle technologies.
TOT: Topology-Aware Optimal Transport For Multimodal Hate Detection
Multimodal hate detection, which aims to identify harmful content online such as memes, is crucial for building a wholesome internet environment.
Cost-aware Defense for Parallel Server Systems against Reliability and Security Failures
Parallel server systems in transportation, manufacturing, and computing heavily rely on dynamic routing using connected cyber components for computation and communication.
EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System
Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction
Implicit Event Argument Extraction seeks to identify arguments that play direct or implicit roles in a given event.
HYPER^2: Hyperbolic Poincare Embedding for Hyper-Relational Link Prediction
Link Prediction, addressing the issue of completing KGs with missing facts, has been broadly studied.
A Hybrid Queuing Model for Coordinated Vehicle Platooning on Mixed-Autonomy Highways: Training and Validation
Platooning of connected and autonomous vehicles (CAVs) is an emerging technology with a strong potential for throughput improvement and fuel reduction.
SRQA: Synthetic Reader for Factoid Question Answering
This model enhances the question answering system in the multi-document scenario from three aspects: model structure, optimization goal, and training method, corresponding to Multilayer Attention (MA), Cross Evidence (CE), and Adversarial Training (AT) respectively.
V2I Connectivity-Based Dynamic Queue-Jump Lane for Emergency Vehicles: A Deep Reinforcement Learning Approach
Emergency vehicle (EMV) service is a key function of cities and is exceedingly challenging due to urban traffic congestion.
Optimizing Coordinated Vehicle Platooning: An Analytical Approach Based on Stochastic Dynamic Programming
When a CAV approaches the junction, a system operator determines whether the CAV will merge into the platoon ahead according to the positions and speeds of the CAV and the platoon.
Dynamic Queue-Jump Lane for Emergency Vehicles under Partially Connected Settings: A Multi-Agent Deep Reinforcement Learning Approach
Based on pairs of neural networks representing actors and critics for agent vehicles, we develop a multi-agent actor-critic deep reinforcement learning algorithm that handles a varying number of vehicles and a random proportion of connected vehicles in the traffic.
Artificial Intelligence Forecasting of Covid-19 in China
BACKGROUND An alternative to epidemiological models for transmission dynamics of Covid-19 in China, we propose the artificial intelligence (AI)-inspired methods for real-time forecasting of Covid-19 to estimate the size, lengths and ending time of Covid-19 across China.
Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter
In this paper we propose a novel O-D prediction framework combining heterogeneous prediction in graph neural networks and Kalman filter to recognize spatial and temporal patterns simultaneously.
A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension
Meanwhile, we construct A3Net for the WebQA dataset.
A New Statistical Framework for Genetic Pleiotropic Analysis of High Dimensional Phenotype Data
The widely used genetic pleiotropic analysis of multiple phenotypes are often designed for examining the relationship between common variants and a few phenotypes.
Random Bits Regression: a Strong General Predictor for Big Data
To improve accuracy and speed of regressions and classifications, we present a data-based prediction method, Random Bits Regression (RBR).
