@unpublished{PederivaDeMartinoZimmer2023, author = {Pederiva, Marcelo Eduardo and De Martino, Jos{\´e} Mario and Zimmer, Alessandro}, title = {MonoNext: A 3D Monocular Object Detection with ConvNext}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2308.00596}, year = {2023}, abstract = {Autonomous driving perception tasks rely heavily on cameras as the primary sensor for Object Detection, Semantic Segmentation, Instance Segmentation, and Object Tracking. However, RGB images captured by cameras lack depth information, which poses a significant challenge in 3D detection tasks. To supplement this missing data, mapping sensors such as LIDAR and RADAR are used for accurate 3D Object Detection. Despite their significant accuracy, the multi-sensor models are expensive and require a high computational demand. In contrast, Monocular 3D Object Detection models are becoming increasingly popular, offering a faster, cheaper, and easier-to-implement solution for 3D detections. This paper introduces a different Multi-Tasking Learning approach called MonoNext that utilizes a spatial grid to map objects in the scene. MonoNext employs a straightforward approach based on the ConvNext network and requires only 3D bounding box annotated data. In our experiments with the KITTI dataset, MonoNext achieved high precision and competitive performance comparable with state-of-the-art approaches. Furthermore, by adding more training data, MonoNext surpassed itself and achieved higher accuracies.}, language = {en} } @inproceedings{VriesmanJuniorZimmeretal.2023, author = {Vriesman, Daniel and Junior, Alceu Britto and Zimmer, Alessandro and Brandmeier, Thomas}, title = {Multimodal Early Fusion of Automotive Sensors based on Autoencoder Network: An anchor-free approach for Vehicle 3D Detection}, booktitle = {2023 26th International Conference on Information Fusion (FUSION)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-89034-485-4}, doi = {https://doi.org/10.23919/FUSION52260.2023.10224140}, year = {2023}, language = {en} } @article{TabataZimmerCoelhoetal.2023, author = {Tabata, Alan Naoto and Zimmer, Alessandro and Coelho, Leandro Dos Santos and Mariani, Viviana Cocco}, title = {Analyzing CARLA 's performance for 2D object detection and monocular depth estimation based on deep learning approaches}, volume = {2023}, pages = {120200}, journal = {Expert Systems with Applications}, number = {227}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0957-4174}, doi = {https://doi.org/10.1016/j.eswa.2023.120200}, year = {2023}, language = {en} } @inproceedings{PalandurkarChanSilvaetal.2023, author = {Palandurkar, Tanaya Viraj and Chan, Lap Yan and Silva, Joed Lopes Da and Zimmer, Alessandro and Schwarz, Ulrich}, title = {Driver's Chest Position Detection using FMCW Radar Data Collected in a Vehicle Mock-up and CNN}, booktitle = {2023 24th International Radar Symposium (IRS)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-3-944976-34-1}, doi = {https://doi.org/10.23919/IRS57608.2023.10172421}, year = {2023}, language = {en} }