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 …
Automated guided vehicles and autonomous mobile robots for recognition and tracking in civil engineering
Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) have been
widely used recently to solve various engineering problems in logistics, manufacturing, and …
widely used recently to solve various engineering problems in logistics, manufacturing, and …
Real-time anomaly detection and reactive planning with large language models
Monophonic music generation with a given emotion using conditional variational autoencoder
The rapid increase in the importance of human-machine interaction and the accelerating
pace of life pose various challenges for the creators of digital environments. Continuous …
pace of life pose various challenges for the creators of digital environments. Continuous …
Monitoring of perception systems: Deterministic, probabilistic, and learning-based fault detection and identification
This paper investigates runtime monitoring of perception systems. Perception is a critical
component of high-integrity applications of robotics and autonomous systems, such as self …
component of high-integrity applications of robotics and autonomous systems, such as self …
Run-time monitoring of 3D object detection in automated driving systems using early layer neural activation patterns
Monitoring the integrity of object detection for errors within the perception module of
automated driving systems (ADS) is paramount for ensuring safety. Despite recent …
automated driving systems (ADS) is paramount for ensuring safety. Despite recent …
Tokenize the world into object-level knowledge to address long-tail events in autonomous driving
The autonomous driving industry is increasingly adopting end-to-end learning from sensory
inputs to minimize human biases in system design. Traditional end-to-end driving models …
inputs to minimize human biases in system design. Traditional end-to-end driving models …
A system-level view on out-of-distribution data in robotics
When testing conditions differ from those represented in training data, so-called out-of-
distribution (OOD) inputs can mar the reliability of learned components in the modern robot …
distribution (OOD) inputs can mar the reliability of learned components in the modern robot …