Beyond RMSE: Do Machine-Learned Models of Road User Interaction Produce Human-Like Behavior?

Autor: Aravinda Ramakrishnan Srinivasan, Yi-Shin Lin, Morris Antonello, Anthony Knittel, Mohamed Hasan, Majd Hawasly, John Redford, Subramanian Ramamoorthy, Matteo Leonetti, Jac Billington, Richard Romano, Gustav Markkula

Publikováno v: Srinivasan, A R, Lin, Y-S, Antonello, M, Knittel, A, Hasan, M, Hawasly, M, Redford, J, Ramamoorthy, S, Leonetti, M, Billington, J, Romano, R & Markkula, G 2023, ' Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior? ', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 7, pp. 7166-7177 . https://doi.org/10.1109/TITS.2023.3263358

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: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::494f6f79905cff46b9d597bef3e40565
https://doi.org/10.1109/tits.2023.3263358

Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion Profiles

Autor: Morris Antonello, Mihai Dobre, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy

Publikováno v: Antonello, M, Dobre, M, Albrecht, S V, Redford, J & Ramamoorthy, S 2022, Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion Profiles . in Proceedings of the International Conference on Intelligent Robots and Systems (IROS) 2022 . IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), pp. 9829-9836, The 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, Kyoto, Japan, 23/10/22 . https://doi.org/10.1109/IROS47612.2022.9981347

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: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34f301e6eb95f5fc2944e2075290b3e8
http://arxiv.org/abs/2203.08251

RUR53: an unmanned ground vehicle for navigation, recognition, and manipulation

Autor: Stefano Ghidoni, Silvia Gandin, Elisa Tosello, Marco Carraro, Morris Antonello, Nicola Bagarello, Nicola Castaman, Emanuele Menegatti, Roberto Bortoletto, Enrico Pagello, Matteo Munaro

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: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb0871ace4f38ccca4ab0efb9792e6a8
http://hdl.handle.net/11577/3356080

Enhancing semantic segmentation with detection priors and iterated graph cuts for robotics

Autor: Stefano Ghidoni, Morris Antonello, Sabrina Chiesurin

Publikováno v: Engineering Applications of Artificial Intelligence. 90:103467

To foster human–robot interaction, autonomous robots need to understand the environment in which they operate. In this context, one of the main challenges is semantic segmentation, together with the recognition of important objects, which can aid r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::416fcc539c85176fae85cc0578ce0234
https://doi.org/10.1016/j.engappai.2019.103467

Brain-Computer Interface meets ROS: A robotic approach to mentally drive telepresence robots

Autor: Luca Tonin, Emanuele Menegatti, Morris Antonello, Andrea Cimolato, Gloria Beraldo

Publikováno v: ICRA

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: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53cbc942f1d26750e9f3302e9f3628c3
http://arxiv.org/abs/1712.01772