Abstract
Abstract. The ongoing proliferation of remote sensing technologies in the consumer market has been rapidly reshaping the geospatial data acquisition world, and subsequently, the data processing as well as information dissemination processes. Smartphones have clearly established themselves as the primary crowdsourced data generators recently, and provide an incredible volume of remote sensed data with fairly good georeferencing. Besides the potential to map the environment of the smartphone users, they provide information to monitor the dynamic content of the object space. For example, real-time traffic monitoring is one of the most known and widely used real-time crowdsensed application, where the smartphones in vehicles jointly contribute to an unprecedentedly accurate traffic flow estimation. Now we are witnessing another milestone to happen, as driverless vehicle technologies will become another major source of crowdsensed data. Due to safety concerns, the requirements for sensing are higher, as the vehicles should sense other vehicles and the road infrastructure under any condition, not just daylight in favorable weather conditions, and at very fast speed. Furthermore, the sensing is based on using redundant and complementary sensor streams to achieve a robust object space reconstruction, needed to avoid collisions and maintain normal travel patterns. At this point, the remote sensed data in assisted and autonomous vehicles are discarded, or partially recorded for R&D purposes. However, in the long run, as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies mature, recording data will become a common place, and will provide an excellent source of geospatial information for road mapping, traffic monitoring, etc. This paper reviews the key characteristics of crowdsourced vehicle data based on experimental data, and then the processing aspects, including the Data Science and Deep Learning components.
Highlights
The past decade has seen phenomenal developments in sensor technologies, and our environment is continuously observed by an ever growing network of navigation, imaging, mapping and a variety of other sensors
People tend to prefer them compared to dashboard built-in navigation systems due to the currency of the data
This paper looks into these aspect of Autonomous vehicle (AV) technologies, in other words, the potential of crowdsensing to acquire geospatial data along transportation corridors and cities
Summary
The past decade has seen phenomenal developments in sensor technologies, and our environment is continuously observed by an ever growing network of navigation, imaging, mapping and a variety of other sensors. Smartphones represent the highest sensor integration on any mobile platform, they have 8-10 built-in sensors that make these devices extremely powerful navigation and imaging/mapping tools These devices provide an easy access to other sensor deployed in our daily life, such as wearable technologies and smart homes. An important aspect of the acquired sensor data is that it typically comes with location information While this is the primary information source for the smartphone based navigation apps, the use of the spatial context of the sensor data is still not fully exploited. The Smart City concept is based on fully exploiting the technology potential to use and share information to make the life of people living in big and dense urban areas better by improving all the services provided by companies and governments (Su et al, 2011). This paper looks into these aspect of AV technologies, in other words, the potential of crowdsensing to acquire geospatial data along transportation corridors and cities
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