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of 32
pro vyhledávání: '"Antonello, Morris"'
The ability to anticipate pedestrian motion changes is a critical capability for autonomous vehicles. In urban environments, pedestrians may enter the road area and create a high risk for driving, and it is important to identify these cases. Typical
Externí odkaz:
http://arxiv.org/abs/2305.15942
Autor:
Hawasly, Majd, Sadeghi, Jonathan, Antonello, Morris, Albrecht, Stefano V., Redford, John, Ramamoorthy, Subramanian
Achieving safe and robust autonomy is the key bottleneck on the path towards broader adoption of autonomous vehicles technology. This motivates going beyond extrinsic metrics such as miles between disengagement, and calls for approaches that embody s
Externí odkaz:
http://arxiv.org/abs/2208.00096
Autor:
Srinivasan, Aravinda Ramakrishnan, Lin, Yi-Shin, Antonello, Morris, Knittel, Anthony, Hasan, Mohamed, Hawasly, Majd, Redford, John, Ramamoorthy, Subramanian, Leonetti, Matteo, Billington, Jac, Romano, Richard, Markkula, Gustav
Autonomous vehicles use a variety of sensors and machine-learned models to predict the behavior of surrounding road users. Most of the machine-learned models in the literature focus on quantitative error metrics like the root mean square error (RMSE)
Externí odkaz:
http://arxiv.org/abs/2206.11110
Autor:
Antonello, Morris, Dobre, Mihai, Albrecht, Stefano V., Redford, John, Ramamoorthy, Subramanian
Motion prediction of road users in traffic scenes is critical for autonomous driving systems that must take safe and robust decisions in complex dynamic environments. We present a novel motion prediction system for autonomous driving. Our system is b
Externí odkaz:
http://arxiv.org/abs/2203.08251
This paper shows and evaluates a novel approach to integrate a non-invasive Brain-Computer Interface (BCI) with the Robot Operating System (ROS) to mentally drive a telepresence robot. Controlling a mobile device by using human brain signals might im
Externí odkaz:
http://arxiv.org/abs/1712.01772
Autor:
Castaman, Nicola, Tosello, Elisa, Antonello, Morris, Bagarello, Nicola, Gandin, Silvia, Carraro, Marco, Munaro, Matteo, Bortoletto, Roberto, Ghidoni, Stefano, Menegatti, Emanuele, Pagello, Enrico
This paper proposes RUR53: an Unmanned Ground Vehicle able to autonomously navigate through, identify, and reach areas of interest; and there recognize, localize, and manipulate work tools to perform complex manipulation tasks. The proposed contribut
Externí odkaz:
http://arxiv.org/abs/1711.08764
This paper deals with the problem of detecting fallen people lying on the floor by means of a mobile robot equipped with a 3D depth sensor. In the proposed algorithm, inspired by semantic segmentation techniques, the 3D scene is over-segmented into s
Externí odkaz:
http://arxiv.org/abs/1703.03349
Publikováno v:
In Engineering Applications of Artificial Intelligence April 2020 90
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