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1. Sharing the Sidewalk: Observing Delivery Robot Interactions with Pedestrians during a Pilot in Pittsburgh, PA

   https://doi.org/10.3390/mti7050053  

   Weinberg, David; Dwyer, Healy; Fox, Sarah E.; Martelaro, Nikolas (May 2023, Multimodal Technologies and Interaction)

   Sidewalk delivery robots are being deployed as a form of last-mile delivery. While many such robots have been deployed on college campuses, fewer have been piloted on public sidewalks. Furthermore, there have been few observational studies of robots and their interactions with pedestrians. To better understand how sidewalk robots might integrate into public spaces, the City of Pittsburgh, Pennsylvania conducted a pilot of sidewalk delivery robots to understand possible uses and the challenges that could arise in interacting with people in the city. Our team conducted ethnographic observations and intercept interviews to understand how residents perceived of and interacted with sidewalk delivery robots over the course of the public pilot. We found that people with limited knowledge about the robots crafted stories about their purpose and function. We observed the robots causing distractions and obstructions with different sidewalk users (including children and dogs), witnessed people helping immobilized robots, and learned about potential accessibility issues that the robots may pose. Based on our findings, we contribute a set of recommendations for future pilots, as well as questions to guide future design for robots in public spaces. 

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2. A Framework for Open Civic Design: Integrating Public Participation, Crowdsourcing, and Design Thinking

   https://doi.org/10.1145/3487607  

   Reynante, Brandon; Dow, Steven P.; Mahyar, Narges (October 2021, Digital Government: Research and Practice)

   Civic problems are often too complex to solve through traditional top-down strategies. Various governments and civic initiatives have explored more community-driven strategies where citizens get involved with defining problems and innovating solutions. While certain people may feel more empowered, the public at large often does not have accessible, flexible, and meaningful ways to engage. Prior theoretical frameworks for public participation typically offer a one-size-fits-all model based on face-to-face engagement and fail to recognize the barriers faced by even the most engaged citizens. In this article, we explore a vision for open civic design where we integrate theoretical frameworks from public engagement, crowdsourcing, and design thinking to consider the role technology can play in lowering barriers to large-scale participation, scaffolding problem-solving activities, and providing flexible options that cater to individuals’ skills, availability, and interests. We describe our novel theoretical framework and analyze the key goals associated with this vision: (1) to promote inclusive and sustained participation in civics; (2) to facilitate effective management of large-scale participation; and (3) to provide a structured process for achieving effective solutions. We present case studies of existing civic design initiatives and discuss challenges, limitations, and future work related to operationalizing, implementing, and testing this framework. 

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3. Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery

   https://doi.org/10.1016/j.compenvurbsys.2023.101950  

   Hosseini, Maryam; Sevtsuk, Andres; Miranda, Fabio; Cesar, Roberto M.; Silva, Claudio T. (April 2023, Computers, Environment and Urban Systems)

   While cities around the world are increasingly promoting streets and public spaces that prioritize pedestrians over vehicles, significant data gaps have made pedestrian mapping, analysis, and modeling challenging to carry out. Most cities, even in industrialized economies, still lack information about the location and connectivity of their sidewalks, making it difficult to implement research on pedestrian infrastructure and holding the technology industry back from developing accurate, location-based Apps for pedestrians, wheelchair users, street vendors, and other sidewalk users. To address this gap, we have designed and implemented an end-to-end open-source tool— Tile2Net —for extracting sidewalk, crosswalk, and footpath polygons from orthorectified aerial imagery using semantic segmentation. The segmentation model, trained on aerial imagery from Cambridge, MA, Washington DC, and New York City, offers the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles, which can be used to generate planimetric sidewalk data in North American cities. Tile2Net also generates pedestrian networks from the resulting polygons, which can be used to prepare datasets for pedestrian routing applications. The work offers a low-cost and scalable data collection methodology for systematically generating sidewalk network datasets, where orthorectified aerial imagery is available, contributing to over-due efforts to equalize data opportunities for pedestrians, particularly in cities that lack the resources necessary to collect such data using more conventional methods. 

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4. Surveying Sidewalk Materials for and by Individuals Who Are Blind or Have Low Vision: Audio Data Collection and Classification

   Liu, J; Lam, W P; Zhu, Z; Tang, H (March 2024, International Conference on SMART MULTIMEDIA)

   Navigating safely and independently presents considerable challenges for people who are blind or have low vision (BLV), as it re- quires a comprehensive understanding of their neighborhood environments. Our user study reveals that understanding sidewalk materials and objects on the sidewalks plays a crucial role in navigation tasks. This paper presents a pioneering study in the field of navigational aids for BLV individuals. We investigate the feasibility of using auditory data, specifically the sounds produced by cane tips against various sidewalk materials, to achieve material identification. Our approach utilizes ma- chine learning and deep learning techniques to classify sidewalk materials solely based on audio cues, marking a significant step towards empowering BLV individuals with greater autonomy in their navigation. This study contributes in two major ways: Firstly, a lightweight and practical method is developed for volunteers or BLV individuals to autonomously collect auditory data of sidewalk materials using a microphone-equipped white cane. This innovative approach transforms routine cane usage into an effective data-collection tool. Secondly, a deep learning-based classifier algorithm is designed that leverages a dual architecture to enhance audio feature extraction. This includes a pre-trained Convolutional Neural Network (CNN) for regional feature extraction from two-dimensional Mel-spectrograms and a booster module for global feature enrichment. Experimental results indicate that the optimal model achieves an accuracy of 80.96% using audio data only, which can effectively recognize sidewalk materials. 

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5. Surveying Sidewalk Materials for and by Individuals Who Are Blind or Have Low Vision: Audio Data Collection and Classification

   Liu, J; Lam, W P; Zhu, Z; Tang, H (March 2024, International Conference on SMART MULTIMEDIA)

   Navigating safely and independently presents considerable challenges for people who are blind or have low vision (BLV), as it re- quires a comprehensive understanding of their neighborhood environments. Our user study reveals that understanding sidewalk materials and objects on the sidewalks plays a crucial role in navigation tasks. This paper presents a pioneering study in the field of navigational aids for BLV individuals. We investigate the feasibility of using auditory data, specifically the sounds produced by cane tips against various sidewalk materials, to achieve material identification. Our approach utilizes ma- chine learning and deep learning techniques to classify sidewalk materials solely based on audio cues, marking a significant step towards empowering BLV individuals with greater autonomy in their navigation. This study contributes in two major ways: Firstly, a lightweight and practical method is developed for volunteers or BLV individuals to autonomously collect auditory data of sidewalk materials using a microphone-equipped white cane. This innovative approach transforms routine cane usage into an effective data-collection tool. Secondly, a deep learning-based classifier algorithm is designed that leverages a dual architecture to enhance audio feature extraction. This includes a pre-trained Convolutional Neural Network (CNN) for regional feature extraction from two-dimensional Mel-spectrograms and a booster module for global feature enrichment. 

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