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Found 17 papers, 2 papers with code
Learn to Tour: Operator Design For Solution Feasibility Mapping in Pickup-and-delivery Traveling Salesman Problem
We make a comparison of our method and baselines, including classic OR algorithms and existing learning methods.
Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNN), has been booming in science and engineering fields.
TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification
TrafficFlowGAN adopts a normalizing flow model as the generator to explicitly estimate the data likelihood.
Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks
This paper aims to quantify uncertainty in traffic state estimation (TSE) using the generative adversarial network based physics-informed deep learning (PIDL).
A Unified Network Equilibrium for E-Hailing Platform Operation and Customer Mode Choice
This paper aims to combine both economic and network user equilibrium for ride-sourcing and ride-pooling services, while endogenously optimizing the pooling sequence of two origin-destination (OD) pairs.
A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation
Traffic state estimation (TSE) bifurcates into two categories, model-driven and data-driven (e. g., machine learning, ML), while each suffers from either deficient physics or small data.
CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles
A case study is performed on a 2-by-2 road network located in State College, Pennsylvania, USA, to further demonstrate the effectiveness of the proposed algorithm under real-world scenarios.
Physics-Informed Deep Learning for Traffic State Estimation
This paper focuses on highway TSE with observed data from loop detectors, using traffic density as the traffic variables.
A Physics-Informed Deep Learning Paradigm for Car-Following Models
We design physics-informed deep learning car-following (PIDL-CF) architectures encoded with two popular physics-based models - IDM and OVM, on which acceleration is predicted for four traffic regimes: acceleration, deceleration, cruising, and emergency braking.
Dynamic driving and routing games for autonomous vehicles on networks: A mean field game approach
In this paper, we aim to develop a game-theoretic model to solve for AVs's optimal driving strategies of velocity control in the interior of a road link and route choice at a junction node.
Autonomous Vehicles
Decision Making
Optimization and Control
Systems and Control
Systems and Control
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment
We show that the routing behavior of intelligent agents is shown to converge to the classical notion of predictive dynamic user equilibrium (DUE) when traffic environments are simulated using dynamic loading models (DNL).
A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning
While reviewing the methodologies, we primarily focus on the following research questions: (1) What scalable driving policies are to control a large number of AVs in mixed traffic comprised of human drivers and uncontrollable AVs?
Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron
Behavior prediction plays an essential role in both autonomous driving systems and Advanced Driver Assistance Systems (ADAS), since it enhances vehicle's awareness of the imminent hazards in the surrounding environment.
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning
In the second case study, an optimal toll charge of $5. 1 is solved using BO, which improves the objective of city planners by 7. 9%, compared to that without any toll charge.
An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset
The Waymo Open Dataset has been released recently, providing a platform to crowdsource some fundamental challenges for automated vehicles (AVs), such as 3D detection and tracking.
Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach
The game is then simulated with numerical examples to investigate the emergence of human drivers' moral hazard, the AV manufacturer's role in traffic safety, and the law maker's role in liability design.
Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning
Vacant taxi drivers' passenger seeking process in a road network generates additional vehicle miles traveled, adding congestion and pollution into the road network and the environment.
