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Found 17 papers, 2 papers with code
Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller
Our method combines the strength of CBF for real-time collision-avoidance control and RRT for long-horizon motion planning, by using CBF-induced neural controller (CBF-INC) to generate control signals that steer the system towards sampled configurations by RRT.
Inherent Diverse Redundant Safety Mechanisms for AI-based Software Elements in Automotive Applications
While various distribution-based methods exist to provide safety mechanisms for AI models, there is a noted lack of systematic assessment of these methods, especially in the context of safety-critical automotive applications.
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
Multimodal federated learning (FL) aims to enrich model training in FL settings where clients are collecting measurements across multiple modalities.
Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems
This study introduces a stochastic deep Koopman (SDK) framework to model the complex behavior of MMSs.
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center
In practice, however, it is observed that autoencoders can generalize beyond the normal class and achieve a small reconstruction error on some of the anomalous samples.
Robust Multiview Multimodal Driver Monitoring System Using Masked Multi-Head Self-Attention
Driver Monitoring Systems (DMSs) are crucial for safe hand-over actions in Level-2+ self-driving vehicles.
A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis
In the automotive industry, the full cycle of managing in-use vehicle quality issues can take weeks to investigate.
FIR-based Future Trajectory Prediction in Nighttime Autonomous Driving
In order to minimize false collision warnings, in our multi-step framework, first, the large animal is accurately detected and a preliminary risk level is predicted for it and low-risk animals are discarded.
Using simulation to quantify the performance of automotive perception systems
The simulation can be used to characterize system performance or to test its performance under conditions that are difficult to measure (e. g., nighttime for automotive perception systems).
Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index
We further discuss multiple aspects of model monitoring and robustness that need to be analyzed \emph{simultaneously} to achieve robustness for industry safety standards.
A Survey on Evaluation Metrics for Synthetic Material Micro-Structure Images from Generative Models
The evaluation of synthetic micro-structure images is an emerging problem as machine learning and materials science research have evolved together.
Real-Time Driver Monitoring Systems through Modality and View Analysis
Driver distractions are known to be the dominant cause of road accidents.
Towards Accurate and Robust Classification in Continuously Transitioning Industrial Sprays with Mixup
Image classification with deep neural networks has seen a surge of technological breakthroughs with promising applications in areas such as face recognition, medical imaging, and autonomous driving.
Virtual Rings on Highways: Traffic Control by Connected Automated Vehicles
This work gives introduction to traffic control by connected automated vehicles.
BTranspose: Bottleneck Transformers for Human Pose Estimation with Self-Supervised Pre-Training
In this paper, we consider the recently proposed Bottleneck Transformers [2], which combine CNN and multi-head self attention (MHSA) layers effectively, and we integrate it with a Transformer encoder and apply it to the task of 2D human pose estimation.
Stochastic Adversarial Koopman Model for Dynamical Systems
Specifically, the latent encoding of the system is modeled as a Gaussian, and is advanced in time by using an auxiliary neural network that outputs two Koopman matrices $K_{\mu}$ and $K_{\sigma}$.
Deep Adversarial Koopman Model for Reaction-Diffusion systems
Reaction-diffusion systems are ubiquitous in nature and in engineering applications, and are often modeled using a non-linear system of governing equations.
