3 citations · 3 across the 7 of their papers we have counts for
7 papers
Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller
Mingxin Yu, Chenning Yu, M-Mahdi Naddaf-Sh +3
Sampling-based motion planning methods for manipulators in crowded environments often suffer from expensive collision checking and high sampling complexity, which make them difficu…
Inherent Diverse Redundant Safety Mechanisms for AI-based Software Elements in Automotive Applications
Mandar Pitale, Alireza Abbaspour, Devesh Upadhyay
This paper explores the role and challenges of Artificial Intelligence (AI) algorithms, specifically AI-based software elements, in autonomous driving systems. These AI systems are…
FIR-based Future Trajectory Prediction in Nighttime Autonomous Driving
Alireza Rahimpour, Navid Fallahinia, Devesh Upadhyay +1
The performance of the current collision avoidance systems in Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) can be drastically affected by low light and ad…
A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis
Qian Wang, Huanyi Shui, Thi Tu Trinh Tran +4
In the automotive industry, the full cycle of managing in-use vehicle quality issues can take weeks to investigate. The process involves isolating root causes, defining and impleme…
Using simulation to quantify the performance of automotive perception systems
Zhenyi Liu, Devesh Shah, Alireza Rahimpour +3
The design and evaluation of complex systems can benefit from a software simulation - sometimes called a digital twin. The simulation can be used to characterize system performance…
Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index
Aria Khademi, Michael Hopka, Devesh Upadhyay
Safety goes first. Meeting and maintaining industry safety standards for robustness of artificial intelligence (AI) and machine learning (ML) models require continuous monitoring f…