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Found 14 papers, 3 papers with code
On the Safety of Connected Cruise Control: Analysis and Synthesis with Control Barrier Functions
Finally, we combine the analysis of safety measures and the corresponding stability charts to synthesize safety-critical connected cruise controllers using CBFs.
Coordination for Connected Automated Vehicles at Merging Roadways in Mixed Traffic Environment
In this paper, we present an optimal control framework to address motion coordination of connected automated vehicles (CAVs) in the presence of human-driven vehicles (HDVs) in merging scenarios.
Connected Cruise and Traffic Control for Pairs of Connected Automated Vehicles
This paper considers mixed traffic consisting of connected automated vehicles equipped with vehicle-to-everything (V2X) connectivity and human-driven vehicles.
Energy-efficient Reactive and Predictive Connected Cruise Control
Reactive and predictive controllers are proposed.
Disturbance Observers for Robust Safety-critical Control with Control Barrier Functions
This work provides formal safety guarantees for control systems with disturbance.
Learning Time Delay Systems with Neural Ordinary Differential Equations
A novel way of using neural networks to learn the dynamics of time delay systems from sequential data is proposed.
Control Barrier Functionals: Safety-critical Control for Time Delay Systems
The theory of control barrier functions, that provides formal safety guarantees for delay-free systems, is extended to systems with state delay.
Input-to-State Safety with Input Delay in Longitudinal Vehicle Control
Safe longitudinal control is discussed for a connected automated truck traveling behind a preceding connected vehicle.
Energy-efficient Connected Cruise Control with Lean Penetration of Connected Vehicles
This paper focuses on energy-efficient longitudinal controller design for a connected automated truck that travels in mixed traffic consisting of connected and non-connected vehicles.
Virtual Rings on Highways: Traffic Control by Connected Automated Vehicles
This work gives introduction to traffic control by connected automated vehicles.
Safety-Critical Control of Compartmental Epidemiological Models with Measurement Delays
We introduce a methodology to guarantee safety against the spread of infectious diseases by viewing epidemiological models as control systems and by considering human interventions (such as quarantining or social distancing) as control input.
Feed-forward Neural Networks with Trainable Delay
In this paper we build a bridge between feed-forward neural networks and delayed dynamical systems.
Control Regularization for Reduced Variance Reinforcement Learning
We show that functional regularization yields a bias-variance trade-off, and propose an adaptive tuning strategy to optimize this trade-off.
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks
Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process.
