24 citations · 62 across the 12 of their papers we have counts for
12 papers
Synergistic Simplex: Cooperative Runtime Assurance for Safety-Critical Autonomous Systems
Ayoosh Bansal, Mikael Yeghiazaryan, Artyom Khachatryan +4
Autonomous systems increasingly rely on machine-learning (ML) components for safety-critical tasks such as perception and control in autonomous vehicles (AVs). While ML enables ess…
Verification and Validation of a Vision-Based Landing System for Autonomous VTOL Air Taxis
Ayoosh Bansal, Duo Wang, Mikael Yeghiazaryan +9
Autonomous air taxis are poised to revolutionize urban mass transportation, however, ensuring their safety and reliability remains an open challenge. Validating autonomy solutions…
Bayesian Data Augmentation and Training for Perception DNN in Autonomous Aerial Vehicles
Ashik E Rasul, Humaira Tasnim, Hyung-Jin Yoon +5
Learning-based solutions have enabled incredible capabilities for autonomous systems. Autonomous vehicles, both aerial and ground, rely on DNN for various integral tasks, including…
Synergistic Perception and Control Simplex for Verifiable Safe Vertical Landing
Ayoosh Bansal, Yang Zhao, James Zhu +6
Perception, Planning, and Control form the essential components of autonomy in advanced air mobility. This work advances the holistic integration of these components to enhance the…
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…
Ellipsis: Towards Efficient System Auditing for Real-Time Systems
Ayoosh Bansal, Anant Kandikuppa, Chien-Ying Chen +3
System auditing is a powerful tool that provides insight into the nature of suspicious events in computing systems, allowing machine operators to detect and subsequently investigat…
LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving
Jiyang Chen, Simon Yu, Rohan Tabish +4
Object detection in state-of-the-art Autonomous Vehicles (AV) framework relies heavily on deep neural networks. Typically, these networks perform object detection uniformly on the…
Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles
Ayoosh Bansal, Jayati Singh, Micaela Verucchi +2
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,…
SchedGuard: Protecting against Schedule Leaks Using Linux Containers
Jiyang Chen, Tomasz Kloda, Ayoosh Bansal +6
Real-time systems have recently been shown to be vulnerable to timing inference attacks, mainly due to their predictable behavioral patterns. Existing solutions such as schedule ra…
Sidebar: Scratchpad Based Communication Between CPUs and Accelerators
Ayoosh Bansal, Chance Coats, Evan Lissoos +1
Hardware accelerators for neural networks have shown great promise for both performance and power. These accelerators are at their most efficient when optimized for a fixed functio…
Cache Where you Want! Reconciling Predictability and Coherent Caching
Ayoosh Bansal, Jayati Singh, Yifan Hao +3
Real-time and cyber-physical systems need to interact with and respond to their physical environment in a predictable time. While multicore platforms provide incredible computation…