2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020
DOI: 10.1109/itsc45102.2020.9294731
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Cited by 11 publications
(15 citation statements)
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References 17 publications
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“…This network applies to the RGB and Optical Flow layers for the first time to obtain low-level feature maps and then combine the two encoded low-level feature maps. Dilated Convolution allows for a broader receptive area with high-resolution information [35]. It then uses the final forecast.…”
Section: Convolutional Neural Network (Cnn) Modelmentioning
confidence: 99%
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“…This network applies to the RGB and Optical Flow layers for the first time to obtain low-level feature maps and then combine the two encoded low-level feature maps. Dilated Convolution allows for a broader receptive area with high-resolution information [35]. It then uses the final forecast.…”
Section: Convolutional Neural Network (Cnn) Modelmentioning
confidence: 99%
“…Such complex systems are typically built using "black box" Artificial Intelligence (AI), making them difficult to comprehend for users. It is particularly true in the field of intelligent driving, where the level of automation is continually growing due to the use of cutting-edge AI solutions [35]. Since interpretability and clarity are key factors for increasing confidence and protection, future research into Explainable AI (XAI) in the context of intelligent driving is relevant.…”
Section: Explainable Artificial Intelligencementioning
confidence: 99%
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“…from existing approaches for standard activity recognition based, e.g., on Convolutional Neural Networks (CNNs) [6], [7] and Graph Neural Networks (GNNs) [7], [8]. However, existing driver activity recognition research indicates that there is still a long way to go for an accurate driver assistance [7], [9], [10]. The recognition rates are especially low for (1) changes in data appearance (due to the sensor type or placement) and (2) for categories underrepresented in the training set.…”
Section: Introductionmentioning
confidence: 99%
“…Uncertainty-aware models are vital for safety-critical applications of activity recognition approaches, which range from robotics and manufacturing to autonomous driving and surveillance [7], [26], [28]. While obtaining well-calibrated probability estimates is a growing area in general image recognition [8], [10], this performance aspect did not yet receive any attention in the field of video classification.…”
Section: Introductionmentioning
confidence: 99%