@InProceedings{Flores-BenitesMugrMora:2021:SpFeAt,
               author = "Flores-Benites, Victor and Mugruza-Vassallo, Carlos Andr{\'e}s 
                         and Mora-Colque, Rensso Victor Hugo",
          affiliation = "{Universidad Cat{\'o}lica San Pablo } and {Universidad Nacional 
                         Tecnol{\'o}gica de Lima Sur } and {Universidad Cat{\'o}lica San 
                         Pablo}",
                title = "TVAnet: a spatial and feature-based attention model for 
                         self-driving car",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "visual attention, self-driving, spatial attention, feature-based 
                         attention.",
             abstract = "End-to-end methods facilitate the development of self-driving 
                         models by employing a single network that learns the human driving 
                         style from examples. However, these models face problems of 
                         distributional shift problem, causal confusion, and high variance. 
                         To address these problems we propose two techniques. First, we 
                         propose the priority sampling algorithm, which biases the training 
                         sampling towards unknown observations for the model. Priority 
                         sampling employs a trade-off strategy that incentivizes the 
                         training algorithm to explore the whole dataset. Our results show 
                         uniform training on the dataset, as well as improved performance. 
                         As a second approach, we propose a model based on the theory of 
                         visual attention, called TVAnet, by which selecting relevant 
                         visual information to build an optimal environment representation. 
                         TVAnet employs two visual information selection mechanisms: 
                         spatial and feature-based attention. Spatial attention selects 
                         regions with visual encoding similar to contextual encoding, while 
                         feature-based attention selects features disentangled with useful 
                         information for routine driving. Furthermore, we encourage the 
                         model to recognize new sources of visual information by adding a 
                         bottom-up input. Results in the CoRL-2017 dataset show that our 
                         spatial attention mechanism recognizes regions relevant to the 
                         driving task. TVAnet builds disentangled features with low mutual 
                         dependence. Furthermore, our model is interpretable, facilitating 
                         the understanding of intelligent vehicle behavior. Finally, we 
                         report performance improvements over traditional end-to-end 
                         models.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00043",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00043",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45D3C8H",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45D3C8H",
           targetfile = "109.pdf",
        urlaccessdate = "2024, May 01"
}