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Found 10 papers, 1 papers with code
Observer-Based Environment Robust Control Barrier Functions for Safety-critical Control with Dynamic Obstacles
This paper proposes a safety-critical controller for dynamic and uncertain environments, leveraging a robust environment control barrier function (ECBF) to enhance the robustness against the measurement and prediction uncertainties associated with moving obstacles.
K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles
Thus, we use the Koopman operator to represent the nonlinear dynamics of a vehicle in dynamic lane-keeping situations.
Uncertainty Quantification of Autoencoder-based Koopman Operator
We approximate the Koopman operator in a finite-dimensional space with the autoencoder, while the approximated Koopman has an approximation uncertainty.
RNN Controller for Lane-Keeping Systems with Robustness and Safety Verification
This paper proposes a Recurrent Neural Network (RNN) controller for lane-keeping systems, effectively handling model uncertainties and disturbances.
Classification Method of Road Surface Condition and Type with LiDAR Using Spatiotemporal Information
With the proposed DNN, we obtained the highest accuracy of 98. 0\% and 98. 6\% for two subregions near the vehicle.
A Nonlinear Proportional Integral Disturbance Observer and Motion Control Technique for Permanent Magnet Synchronous Motors
In this paper, we present a Nonlinear-Proportional Integrator (N-PI) disturbance observer (DOB) to enhance the motion tracking of the performance of a surface-mounted Permanent Magnet Synchronous Motor (SPMSM) in rapidly speed varying regions.
Robust Control for Lane Keeping System Using Linear Parameter Varying Approach with Scheduling Variables Reduction
In this paper, to reduce the computational complexity, Principal Component Analysis (PCA)-based parameter reduction is performed to obtain a reduced model with a tighter convex set.
Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture
We employ the encoder-decoder architecture which analyzes the pattern underlying in the past trajectory using the long short-term memory (LSTM) based encoder and generates the future trajectory sequence using the LSTM based decoder.
Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network
Our approach is data-driven and simple to use in that it learns complex behavior of the vehicles from the massive amount of trajectory data through deep neural network model.
Autonomous Braking System via Deep Reinforcement Learning
In this paper, we propose a new autonomous braking system based on deep reinforcement learning.
