The social implementation of small autonomous mobile robots is advancing in delivery and accompanying services. However, inappropriate waiting positions can obstruct pedestrian flow and impair facility operations. Conventional waiting location estimation methods require long-term observation of actual pedestrian walking history, resulting in high introduction costs and environmental dependence. This research proposes a fluid simulation-based method that estimates waiting locations using only floor maps without prior observation. By approximating pedestrian flow as a two-dimensional incompressible fluid, the method extracts waiting candidates from velocity fields determined by environmental geometry. Through comparative verification with agent-based simulation and human subject experiments, we confirmed that the proposed method achieves estimation accuracy equivalent to history-based approaches while requiring no prior observation. The method demonstrates environmental adaptability across simple corridors to complex spaces, with a practical threshold of 0.4 times maximum flow velocity effectively distinguishing appropriate waiting locations. The approach enables rapid deployment and adaptation to layout changes, addressing key challenges in mobile robot social implementation.

Intelligent vehicle perception algorithms often have difficulty accurately analyzing and interpreting images in adverse weather conditions. Snow is a corner case that not only reduces visibility and contrast but also affects the stability of the road environment. While it is possible to train deep learning models on real-world driving datasets in snow weather, obtaining such data can be challenging. Synthesizing snow effects on existing driving datasets is a viable alternative. In this work, we propose a method based on Cycle Consistent Generative Adversarial Networks (CycleGANs) that utilizes additional semantic information to generate snow effects. We apply deep supervision by using intermediate outputs from the last two convolutional layers in the generator as multi-scale supervision signals for training. We collect a small set of driving image data captured under heavy snow as the translation source. We compare the generated images with those produced by various network architectures and evaluate the results qualitatively and quantitatively on the Cityscapes and EuroCity Persons datasets. Experiment results indicate that our model can synthesize realistic snow effects in driving images.

In this research, a two-stage risk object warning method is proposed to generate the auditory and visual warning information simultaneously from the driving scene. The auditory warning module (AWM) is designed as a classification task by combining the rough location and type information as warning sentences and treating each sentence as one class. The visual warning module (VWM) is designed as a weakly supervised method to save the labor-intensive bounding box marking of risk objects. To confirm the effectiveness of the proposed method, we also create a linguistic risk notification (LRN) dataset by describing the driving scenario as several different sentences. The average accuracy of auditory warning is 96.4% for generating the warning sentences. The average accuracy of the weakly supervised visual warning algorithm is 81.3% for getting the risk vehicle localization without any supervisory information.

Through smartphones, user enables to download/listen music anytime and anywhere. As a concept of a future audio player, we propose a framework of "music staging artificial intelligence (AI)". In that framework, audio object signals, e.g. vocal, guitar, bass, drums and keyboards, are assumed to be extracted from stereo music signals. To visualize music as if live performance is virtually conducted, playing motion sequence is estimated by using separated signals. After adjusting the spatial arrangement of audio objects so as to each user prefers it, audio/visual rendering is conducted. We constructed two types of demonstration systems for music staging AI. In the smartphone-based implementation, each user enables to change the spatial arrangement through sliderbar dragging. Since information of user preferable spatial arrangement can be sent from each smartphone to server, it would enable to predict/recommend the user preferable spatial arrangement. In another implementation, head mount display (HMD) was utilized to dive into virtual music live performance. Each user enables to walk/teleport anywhere and audio is then changing corresponding to the user view.