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Found 19 papers, 7 papers with code
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation
Due to the covariate shift issue, existing imitation learning-based simulators often fail to generate stable long-term simulations.
EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning
To address this, we introduce a reinforcement learning based RV that employs a congestion stage classifier to optimize the safety, efficiency, and stability of mixed traffic.
Analyzing Emissions and Energy Efficiency at Unsignalized Real-world Intersections Under Mixed Traffic Control
With at least 30% RVs, CO and HC emissions are reduced by up to 42% and 43%, respectively.
Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records
But in practice, blood testing data is not always available at the point of referrals, so we need methods to leverage multimodal data such as semi-structured and unstructured data for early detection of IA.
Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning
However, defining objectives of RL agents in traffic control and management tasks, as well as aligning policies with these goals through an effective formulation of Markov Decision Process (MDP), can be challenging and often require domain experts in both RL and ITS.
Efficient Quality-Diversity Optimization through Diverse Quality Species
This can be rectified by Quality-Diversity (QD) algorithms, where a population of high-quality and diverse solutions to a problem is preferred.
Mixed Traffic Control and Coordination from Pixels
In certain scenarios, our approach even outperforms using precision observations, e. g., up to 8% increase in average vehicle velocity in the merge environment, despite only using local traffic information as opposed to global traffic information.
Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections
In contrast, without RVs, congestion starts to develop when the traffic demand reaches as low as 200 vehicles per hour.
AutoJoin: Efficient Adversarial Training for Robust Maneuvering via Denoising Autoencoder and Joint Learning
As a result of increasingly adopted machine learning algorithms and ubiquitous sensors, many 'perception-to-control' systems are developed and deployed.
A Generic Graph Sparsification Framework using Deep Reinforcement Learning
The interconnectedness and interdependence of modern graphs are growing ever more complex, causing enormous resources for processing, storage, communication, and decision-making of these graphs.
Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering
We introduce a simple yet effective framework for improving the robustness of learning algorithms against image corruptions for autonomous driving.
Network-wide Multi-step Traffic Volume Prediction using Graph Convolutional Gated Recurrent Neural Network
For instance, our model reduces MAE by 25. 3%, RMSE by 29. 2%, and MAPE by 20. 2%, compared to the state-of-the-art Diffusion Convolutional Recurrent Neural Network (DCRNN) model using the hourly dataset.
Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models
While the prediction accuracy of deep learning models is high, these models' robustness has raised many safety concerns, given that imperceptible perturbations added to input can substantially degrade the model performance.
LRN: Limitless Routing Networks for Effective Multi-task Learning
Multi-task learning (MTL) is a field involved with learning multiple tasks simultaneously typically through using shared model parameters.
Analyzing Effects of The COVID-19 Pandemic on Road Traffic Safety: The Cases of New York City, Los Angeles, and Boston
The COVID-19 pandemic has resulted in significant social and economic impacts throughout the world.
Learning to Control DC Motor for Micromobility in Real Time with Reinforcement Learning
Autonomous micromobility has been attracting the attention of researchers and practitioners in recent years.
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images
For safety of autonomous driving, vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.
Driving through the Lens: Improving Generalization of Learning-based Steering using Simulated Adversarial Examples
To ensure the wide adoption and safety of autonomous driving, the vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.
Imperatives for Virtual Humans
Seemingly since the inception of virtual humans, there has been an effort to make their behaviors more natural and human-like.
