22 citations · 63 across the 7 of their papers we have counts for
7 papers
Simulation Assessment Guidelines towards Independent Safety Assurance of Autonomous Vehicles
Jim Cherian, Martin Slavik, Andrea Piazzoni +3
This Simulation Assessment Guidelines document is a public guidelines document developed by the Centre of Excellence for Testing & Research of AVs - NTU (CETRAN) in collaboration w…
White paper on Selected Environmental Parameters affecting Autonomous Vehicle (AV) Sensors
James Lee Wei Shung, Andrea Piazzoni, Roshan Vijay +2
Autonomous Vehicles (AVs) being developed these days rely on various sensor technologies to sense and perceive the world around them. The sensor outputs are subsequently used by th…
PEM: Perception Error Model for Virtual Testing of Autonomous Vehicles
Andrea Piazzoni, Jim Cherian, Justin Dauwels +1
Even though virtual testing of Autonomous Vehicles (AVs) has been well recognized as essential for safety assessment, AV simulators are still undergoing active development. One par…
CoPEM: Cooperative Perception Error Models for Autonomous Driving
Andrea Piazzoni, Jim Cherian, Roshan Vijay +2
In this paper, we introduce the notion of Cooperative Perception Error Models (coPEMs) towards achieving an effective and efficient integration of V2X solutions within a virtual te…
ViSTA: a Framework for Virtual Scenario-based Testing of Autonomous Vehicles
Andrea Piazzoni, Jim Cherian, Mohamed Azhar +3
In this paper, we present ViSTA, a framework for Virtual Scenario-based Testing of Autonomous Vehicles (AV), developed as part of the 2021 IEEE Autonomous Test Driving AI Test Chal…
Vehicle Ego-Lane Estimation with Sensor Failure Modeling
Augusto Luis Ballardini, Daniele Cattaneo, Rubén Izquierdo +4
We present a probabilistic ego-lane estimation algorithm for highway-like scenarios that is designed to increase the accuracy of the ego-lane estimate, which can be obtained relyin…
Modeling Perception Errors towards Robust Decision Making in Autonomous Vehicles
Andrea Piazzoni, Jim Cherian, Martin Slavik +1
Sensing and Perception (S&P) is a crucial component of an autonomous system (such as a robot), especially when deployed in highly dynamic environments where it is required to react…