Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Sedwards, Sean"'
We consider the problem of cross-sensor domain adaptation in the context of LiDAR-based 3D object detection and propose Stationary Object Aggregation Pseudo-labelling (SOAP) to generate high quality pseudo-labels for stationary objects. In contrast t
Externí odkaz:
http://arxiv.org/abs/2401.04230
Modelling pedestrian behavior is crucial in the development and testing of autonomous vehicles. In this work, we present a hierarchical pedestrian behavior model that generates high-level decisions through the use of behavior trees, in order to produ
Externí odkaz:
http://arxiv.org/abs/2206.01601
We consider the challenge of finding a deterministic policy for a Markov decision process that uniformly (in all states) maximizes one reward subject to a probabilistic constraint over a different reward. Existing solutions do not fully address our p
Externí odkaz:
http://arxiv.org/abs/2201.07958
Autor:
Ilievski, Marko, Sedwards, Sean, Gaurav, Ashish, Balakrishnan, Aravind, Sarkar, Atrisha, Lee, Jaeyoung, Bouchard, Frédéric, De Iaco, Ryan, Czarnecki, Krzysztof
We explore the complex design space of behaviour planning for autonomous driving. Design choices that successfully address one aspect of behaviour planning can critically constrain others. To aid the design process, in this work we decompose the desi
Externí odkaz:
http://arxiv.org/abs/1908.07931
Publikováno v:
International Conference on Quantitative Evaluation of Systems (QEST 2019)
Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge. A number of authors have attempted to address this issue, but there are few publicly-available
Externí odkaz:
http://arxiv.org/abs/1902.04118
We present an algorithm that quickly finds falsifying inputs for hybrid systems, i.e., inputs that steer the system towards violation of a given temporal logic requirement. Our method is based on a probabilistically directed search of an increasingly
Externí odkaz:
http://arxiv.org/abs/1812.04159
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Few real-world hybrid systems are amenable to formal verification, due to their complexity and black box components. Optimization-based falsification---a methodology of search-based testing that employs stochastic optimization---is attracting attenti
Externí odkaz:
http://arxiv.org/abs/1803.06276
Publikováno v:
EPTCS 361, 2022, pp. 25-43
Optimization-based falsification employs stochastic optimization algorithms to search for error input of hybrid systems. In this paper we introduce a simple idea to enhance falsification, namely time staging, that allows the time-causal structure of
Externí odkaz:
http://arxiv.org/abs/1803.03866
Time delays pose an important challenge in networked control systems, which are now ubiquitous. Focusing on switched systems, we introduce a framework that provides an upper bound for errors caused by switching delays. Our framework is based on appro
Externí odkaz:
http://arxiv.org/abs/1712.06311