24 citations · 125 across the 32 of their papers we have counts for
32 papers
Backup Plan Constrained Model Predictive Control with Guaranteed Stability
Ran Tao, Hunmin Kim, Hyung-Jin Yoon +4
This article proposes and evaluates a new safety concept called backup plan safety for path planning of autonomous vehicles under mission uncertainty using model predictive control…
Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings
Hongpeng Cao, Yanbing Mao, Lui Sha +1
This paper proposes the Phy-DRL: a physics-regulated deep reinforcement learning (DRL) framework for safety-critical autonomous systems. The Phy-DRL has three distinguished invaria…
Physical Deep Reinforcement Learning Towards Safety Guarantee
Hongpeng Cao, Yanbing Mao, Lui Sha +1
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. Howe…
Phy-Taylor: Physics-Model-Based Deep Neural Networks
Yanbing Mao, Lui Sha, Huajie Shao +3
Purely data-driven deep neural networks (DNNs) applied to physical engineering systems can infer relations that violate physics laws, thus leading to unexpected consequences. To ad…
Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults
Ayoosh Bansal, Hunmin Kim, Simon Yu +4
Advances in deep learning have revolutionized cyber-physical applications, including the development of Autonomous Vehicles. However, real-world collisions involving autonomous con…
Verifiable Obstacle Detection
Ayoosh Bansal, Hunmin Kim, Simon Yu +4
Perception of obstacles remains a critical safety concern for autonomous vehicles. Real-world collisions have shown that the autonomy faults leading to fatal collisions originate f…