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Found 24 papers, 5 papers with code
Generalizing Cooperative Eco-driving via Multi-residual Task Learning
To address this, we introduce Multi-residual Task Learning (MRTL), a generic learning framework based on multi-task learning that, for a set of task scenarios, decomposes the control into nominal components that are effectively solved by conventional control methods and residual terms which are solved using learning.
Multi-agent Path Finding for Cooperative Autonomous Driving
Anticipating possible future deployment of connected and automated vehicles (CAVs), cooperative autonomous driving at intersections has been studied by many works in control theory and intelligent transportation across decades.
Expert with Clustering: Hierarchical Online Preference Learning Framework
Even more so than the typical recommendation system, it is crucial to minimize regret, because 1) the mobility options directly affect the lives of the users, and 2) the system sustainability relies on sufficient user participation.
Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective
To overcome these challenges and enhance real-world applicability in near-term, we propose a model-free approach employing deep reinforcement learning (DRL) for designing CAV control strategies, showing its reduced overhead in designing and greater scalability and performance compared to model-based methods.
Data-Driven Traffic Reconstruction and Kernel Methods for Identifying Stop-and-Go Congestion
Identifying stop-and-go events (SAGs) in traffic flow presents an important avenue for advancing data-driven research for climate change mitigation and sustainability, owing to their substantial impact on carbon emissions, travel time, fuel consumption, and roadway safety.
Incentive Design for Eco-driving in Urban Transportation Networks
Eco-driving emerges as a cost-effective and efficient strategy to mitigate greenhouse gas emissions in urban transportation networks.
Hybrid System Stability Analysis of Multi-Lane Mixed-Autonomy Traffic
Through examining the influence of the lane-switch frequency on the system's stability, the analysis offers a principled explanation to the traffic break phenomena, and further discovers opportunities for less-intrusive traffic smoothing by employing less frequent lane-switching.
PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP.
Towards Co-operative Congestion Mitigation
The effects of traffic congestion are widespread and are an impedance to everyday life.
Integrated Analysis of Coarse-Grained Guidance for Traffic Flow Stability
While previous theoretical studies consider stability analysis for continuous AV control, this article presents the first integrated theoretical analysis that directly relates the guidance provided to the human drivers to the traffic flow stability outcome.
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning
We show that in comparison to evaluating DRL methods on select MDP instances, evaluating the MDP family often yields a substantially different relative ranking of methods, casting doubt on what methods should be considered state-of-the-art.
Unified Automatic Control of Vehicular Systems with Reinforcement Learning
This is a key challenge to efficient analysis of diverse vehicular and mobility systems.
Learning Eco-Driving Strategies at Signalized Intersections
Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions.
The Braess Paradox in Dynamic Traffic
The Braess's Paradox (BP) is the observation that adding one or more roads to the existing road network will counter-intuitively increase traffic congestion and slow down the overall traffic flow.
Cooperation for Scalable Supervision of Autonomy in Mixed Traffic
A key takeaway is the potential value of cooperation in enabling the deployment of autonomy at scale.
Reinforcement Learning for Mixed Autonomy Intersections
We propose a model-free reinforcement learning method for controlling mixed autonomy traffic in simulated traffic networks with through-traffic-only two-way and four-way intersections.
Understanding the factors driving the opioid epidemic using machine learning
Furthermore, we discovered that, as the epidemic has shifted from legal (i. e., prescription opioids) to illegal (e. g., heroin and fentanyl) drugs in recent years, the correlation of environment, crime and health related variables with the opioid risk has increased significantly while the correlation of economic and socio-demographic variables has decreased.
Learning to Delegate for Large-scale Vehicle Routing
We frame subproblem selection as regression and train a Transformer on a generated training set of problem instances.
SMIL: Multimodal Learning with Severely Missing Modality
A common assumption in multimodal learning is the completeness of training data, i. e., full modalities are available in all training examples.
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP.
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
Furthermore, in single-lane traffic, a small neural network control law with only local observation is found to eliminate stop-and-go traffic - surpassing all known model-based controllers to achieve near-optimal performance - and generalize to out-of-distribution traffic densities.
Noise Reduction Methods for Distantly Supervised Biomedical Relation Extraction
Distant supervision has been applied to automatically generate labeled data for biomedical relation extraction.
Identifying Comparative Structures in Biomedical Text
Comparison sentences are very commonly used by authors in biomedical literature to report results of experiments.
