Artificial intelligence for safety-critical systems in industrial and transportation domains: A survey
Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-
critical systems in which Machine Learning (ML) algorithms learn optimized and safe …
critical systems in which Machine Learning (ML) algorithms learn optimized and safe …
Enforcing hard constraints with soft barriers: Safe reinforcement learning in unknown stochastic environments
It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an
unknown and stochastic environment under hard constraints that require the system state …
unknown and stochastic environment under hard constraints that require the system state …
Polar: A polynomial arithmetic framework for verifying neural-network controlled systems
We present POLAR (The source code can be found at https://github. com/ChaoHuang2018/
POLAR_Tool. The full version of this paper can be found at https://arxiv …
POLAR_Tool. The full version of this paper can be found at https://arxiv …
Learning representation for anomaly detection of vehicle trajectories
Predicting the future trajectories of surrounding vehicles based on their history trajectories is
a critical task in autonomous driving. However, when small crafted perturbations are …
a critical task in autonomous driving. However, when small crafted perturbations are …
Efficient global robustness certification of neural networks via interleaving twin-network encoding
The robustness of deep neural networks has received significant interest recently, especially
when being deployed in safety-critical systems, as it is important to analyze how sensitive …
when being deployed in safety-critical systems, as it is important to analyze how sensitive …
Physics-aware safety-assured design of hierarchical neural network based planner
Neural networks have shown great promises in planning, control, and general decision
making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving …
making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving …
Safety-assured speculative planning with adaptive prediction
Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …
Out-of-distribution detection for deep neural networks with isolation forest and local outlier factor
Deep Neural Networks (DNNs) are extensively deployed in today's safety-critical
autonomous systems thanks to their excellent performance. However, they are known to …
autonomous systems thanks to their excellent performance. However, they are known to …
Waving the double-edged sword: Building resilient cavs with edge and cloud computing
The rapid advancement of edge and cloud computing platforms, vehicular ad-hoc networks,
and machine learning techniques have brought both opportunities and challenges for next …
and machine learning techniques have brought both opportunities and challenges for next …
Safety-driven interactive planning for neural network-based lane changing
Neural network-based driving planners have shown great promises in improving task
performance of autonomous driving. However, it is critical and yet very challenging to ensure …
performance of autonomous driving. However, it is critical and yet very challenging to ensure …