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Found 22 papers, 3 papers with code
Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic
The Speed Planner comprises two modules: a TSE enhancement module and a target speed design module.
Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs
The upper layer is called Speed Planner, and is a centralized optimal control algorithm.
Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test
In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles.
Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data
Finally, we analyze the smoothing effect of the controllers and demonstrate robustness to adding lane-changing into the simulation as well as the removal of downstream information.
Traffic smoothing using explicit local controllers
The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving.
Reducing Detailed Vehicle Energy Dynamics to Physics-Like Models
The energy demand of vehicles, particularly in unsteady drive cycles, is affected by complex dynamics internal to the engine and other powertrain components.
Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments
Autonomous driving systems present promising methods for congestion mitigation in mixed autonomy traffic control settings.
Unified Automatic Control of Vehicular Systems with Reinforcement Learning
This is a key challenge to efficient analysis of diverse vehicular and mobility systems.
Learning energy-efficient driving behaviors by imitating experts
The rise of vehicle automation has generated significant interest in the potential role of future automated vehicles (AVs).
Reachability Analysis for FollowerStopper: Safety Analysis and Experimental Results
The FollowerStopper controller has been demonstrated to dampen stop-and-go traffic waves at low speed, but previous analysis on its relative safety has been limited to upper and lower bounds of acceleration.
Longitudinal Deep Truck: Deep learning and deep reinforcement learning for modeling and control of longitudinal dynamics of heavy duty trucks
Heavy duty truck mechanical configuration is often tailor designed and built for specific truck mission requirements.
Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers
This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular attention to accurate energy values for strongly unsteady driving profiles; and sparse Lagrangian controls via automated vehicles, with a focus on controls that can be executed via existing technology such as adaptive cruise control systems.
Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning
Here, we propose a deep RL framework that dynamically adjusts offsets based on traffic states and preserves the planned phase timings and order derived from model-based methods.
ResiliNet: Failure-Resilient Inference in Distributed Neural Networks
Nevertheless, when a neural network is partitioned and distributed among physical nodes, failure of physical nodes causes the failure of the neural units that are placed on those nodes, which results in a significant performance drop.
Inter-Level Cooperation in Hierarchical Reinforcement Learning
This allows us to draw on connections between communication and cooperation in multi-agent RL, and demonstrate the benefits of increased cooperation between sub-policies on the training performance of the overall policy.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Guardians of the Deep Fog: Failure-Resilient DNN Inference from Edge to Cloud
To articulate deepFogGuard, we introduce the elements and a model for the resiliency of distributed DNN inference.
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.
Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning
This article shows how the recent breakthroughs in Reinforcement Learning (RL) that have enabled robots to learn to play arcade video games, walk or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems.
Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies
Linear causal analysis is central to a wide range of important application spanning finance, the physical sciences, and engineering.
Learning Nash Equilibria in Congestion Games
We show that strong convergence can be guaranteed for a class of algorithms with a vanishing upper bound on discounted regret, and which satisfy an additional condition.
Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors.
