Search Results for author:
Found 11 papers, 1 papers with code
Backup Plan Constrained Model Predictive Control with Guaranteed Stability
To include this new safety concept in control problems, we formulate a feasibility maximization problem aiming to maximize the feasibility of the primary and alternative missions.
Physical Deep Reinforcement Learning: Safety and Unknown Unknowns
In this paper, we propose the Phy-DRL: a physics-model-regulated deep reinforcement learning framework for safety-critical autonomous systems.
Physical Deep Reinforcement Learning Towards Safety Guarantee
Deep reinforcement learning (DRL) has achieved tremendous success in many complex decision-making tasks of autonomous systems with high-dimensional state and/or action spaces.
Phy-Taylor: Physics-Model-Based Deep Neural Networks
To do so, the PhN augments neural network layers with two key components: (i) monomials of Taylor series expansion of nonlinear functions capturing physical knowledge, and (ii) a suppressor for mitigating the influence of noise.
Synergistic Redundancy: Towards Verifiable Safety for Autonomous Vehicles
The synergistic safety layer uses only verifiable and logically analyzable software to fulfill its tasks.
Verifiable Obstacle Detection
Perception of obstacles remains a critical safety concern for autonomous vehicles.
LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving
Object detection in state-of-the-art Autonomous Vehicles (AV) framework relies heavily on deep neural networks.
Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles
Commonly used metrics for evaluation of object detection systems (precision, recall, mAP) do not give complete information about their suitability of use in safety critical tasks, like obstacle detection for collision avoidance in Autonomous Vehicles (AV).
Backup Plan Constrained Model Predictive Control
To incorporate this new safety concept in control problems, we formulate a feasibility maximization problem that adopts additional (virtual) input horizons toward the alternative missions on top of the input horizon toward the primary mission.
SL1-Simplex: Safe Velocity Regulation of Self-Driving Vehicles in Dynamic and Unforeseen Environments
Due to the high dependence of vehicle dynamics on the driving environments, the proposed Simplex leverages the finite-time model learning to timely learn and update the vehicle model for $\mathcal{L}_{1}$ adaptive controller, when any deviation from the safety envelope or the uncertainty measurement threshold occurs in the unforeseen driving environments.
Formalism for Supporting the Development of Verifiably Safe Medical Guidelines with Statecharts
The framework allows computer scientists to work together with medical professionals to transform medical best practice guidelines into executable statechart models, Yakindu in particular, so that medical functionalities and properties can be quickly prototyped and validated.
