Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Piazzoni, Andrea"'
Autor:
Cherian, Jim, Slavik, Martin, Piazzoni, Andrea, Vijay, Roshan, Azhar, Mohamed, de Boer, Niels
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 with the Land Transport Authority (LTA) of Singapore. It is primarily i
Externí odkaz:
http://arxiv.org/abs/2310.00924
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 the Automated Driving System (ADS) onboard the vehicle to make decisions
Externí odkaz:
http://arxiv.org/abs/2309.02673
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 1, pp. 670-681, Jan. 2024
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 particularly challenging question is to effectively include the Sensing a
Externí odkaz:
http://arxiv.org/abs/2302.11919
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 test environment. We focus our analysis on the occlusion problem in the
Externí odkaz:
http://arxiv.org/abs/2211.11175
Autor:
Piazzoni, Andrea, Cherian, Jim, Azhar, Mohamed, Yap, Jing Yew, Shung, James Lee Wei, Vijay, Roshan
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 Challenge. Scenario-based virtual testing aims to construct specific chall
Externí odkaz:
http://arxiv.org/abs/2109.02529
Autor:
Ballardini, Augusto Luis, Cattaneo, Daniele, Izquierdo, Rubén, Alonso, Ignacio Parra, Piazzoni, Andrea, Sotelo, Miguel Ángel, Sorrenti, Domenico Giorgio
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 relying only on a noisy line detector and tracker. The contribution relies o
Externí odkaz:
http://arxiv.org/abs/2002.01913
Publikováno v:
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Main track (2020). Pages 3494-3500
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 to unexpected situations. This is particularly true in case of Autono
Externí odkaz:
http://arxiv.org/abs/2001.11695
Akademický článek
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Akademický článek
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Publikováno v:
IEEE Transactions on Intelligent Transportation Systems; January 2024, Vol. 25 Issue: 1 p670-681, 12p
