Crowdsourcing good landmarks for in-vehicle navigation systems

Augmenting navigation systems with landmarks has been posited as a method of improving the effectiveness of the technology and enhancing drivers’ engagement with the environment. However, good navigational landmarks are both laborious to collect and difficult to define. This research aimed to devise a game concept, which could be played by passengers in cars, and would collect useful landmark data as a by-product. The paper describes how a virtual graffiti tagging game concept was created and tested during on-road trials with 38 participants. The data collected in the road trials were then validated using a survey, in which 100 respondents assessed the quality of the landmarks collected and their potential for reuse in navigation applications. Players of the game displayed a consensus in choosing where to place their graffiti tags with over 30% of players selecting the same object to tag in 10 of the 12 locations. Furthermore, significant correlation was found between how highly landmarks were rated in the survey and how frequently they were tagged during the game. The research provides evidence that using crowdsourcing games to collect landmarks does not require large numbers of people, or extensive coverage of an area, to produce suitable candidate landmarks for navigation

Iterative Design and Prototyping of Computer Vision Mediated Remote Sighted Assistance

Remote sighted assistance (RSA) is an emerging navigational aid for people with visual impairments (PVI). Using scenario-based design to illustrate our ideas, we developed a prototype showcasing potential applications for computer vision to support RSA interactions. We reviewed the prototype demonstrating real-world navigation scenarios with an RSA expert, and then iteratively refined the prototype based on feedback. We reviewed the refined prototype with 12 RSA professionals to evaluate the desirability and feasibility of the prototyped computer vision concepts. The RSA expert and professionals were engaged by, and reacted insightfully and constructively to the proposed design ideas. We discuss what we learned about key resources, goals, and challenges of the RSA prosthetic practice through our iterative prototype review, as well as implications for the design of RSA systems and the integration of computer vision technologies into RSA

Predefining regionalised environments for assisted navigation : does incorporating regions into navigation instructions assist a user’s spatial understanding of the environment they aretravelling through?

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThis thesis proposes introducing pre-defined regionalised areas into navigation instructions to allow drivers to learn more about the environment they’re travelling through. Following detailed navigation instructions, drivers are no longer required to learn about and understand their environment, which leaves drivers reliant on these navigation devices. An experiment using a virtual environment was conducted to evaluate if a group with additional regional instructions would complete tasks more effectively than a group with traditional instructions. While the regional group performed better on all accounts, statistically significant results were only found in three of ten variables. There were however, large differences in task completion rates, suggesting that incorporating pre-defined regions to navigation instructions does make a difference in drivers’ understanding of their environment

Improving public transit accessibility for blind riders by crowdsourcing bus stop landmark locations with Google street view

Low-vision and blind bus riders often rely on known physical landmarks to help locate and verify bus stop locations (e.g., by searching for a shelter, bench, newspaper bin). However, there are currently few, if any, methods to determine this information a priori via computational tools or services. In this paper, we introduce and evaluate a new scalable method for collecting bus stop location and landmark descriptions by combining online crowdsourcing and Google Street View (GSV). We conduct and report on three studies in particular: (i) a formative interview study of 18 people with visual impairments to inform the design of our crowdsourcing tool; (ii) a comparative study examining differences between physical bus stop audit data and audits conducted virtually with GSV; and (iii) an online study of 153 crowd workers on Amazon Mechanical Turk to examine the feasibility of crowdsourcing bus stop audits using our custom tool with GSV. Our findings reemphasize the importance of landmarks in non-visual navigation, demonstrate that GSV is a viable bus stop audit dataset, and show that minimally trained crowd workers can find and identify bus stop landmarks with 82.5 % accuracy across 150 bus stop locations (87.3 % with simple quality control)

Improving public transit accessibility for blind riders by crowdsourcing bus stop landmark locations with Google street view: An extended analysis

Low-vision and blind bus riders often rely on known physical landmarks to help locate and verify bus stop locations (e.g., by searching for an expected shelter, bench, or newspaper bin). However, there are currently few, if any, methods to determine this information a priori via computational tools or services. In this article, we introduce and evaluate a new scalable method for collecting bus stop location and landmark descriptions by combining online crowdsourcing and Google Street View (GSV). We conduct and report on three studies: (i) a formative interview study of 18 people with visual impairments to inform the design of our crowdsourcing tool, (ii) a comparative study examining differences between physical bus stop audit data and audits conducted virtually with GSV, and (iii) an online study of 153 crowd workers on Amazon Mechanical Turk to examine the feasibility of crowdsourcing bus stop audits using our custom tool with GSV. Our findings reemphasize the importance of landmarks in nonvisual navigation, demonstrate that GSV is a viable bus stop audit dataset, and show that minimally trained crowd workers can find and identify bus stop landmarks with 82.5% accuracy across 150 bus stop locations (87.3% with simple quality control). </jats:p