Autonomous vehicles (AVs) are starting to hit our roads. It is only a matter of time until the technological challenges still facing full AV implementation are solved, and legal, social, and transport issues related to AVs become part of the public discussion. AVs have the potential to become a major catalyst for urban transformation. To explore some of these transformations, first, we discuss the possibility of decoupling the many functions of urban vehicles from the form factor (without drivers, do cars need to look like they look today?). Second, we question whether AVs will lead to more or fewer cars on the roads, highlighting the synergies between AVs and ride-sharing schemes. Third, with AVs as part of multimodal and sharing-mobility systems, millions of square kilometers currently used for parking spaces might be liberated, or even change the way we design road space. Fourth, freed from the fatigue related to traffic, we question whether AVs would make people search for home locations farther from cities, increasing urban sprawl, or would rather attract more residents to city centers, also freed from congestion and pollution. Fifth, depending on responses to the previous questions and innovative traffic algorithms, we ask whether AVs will demand more or less road infrastructure. We conclude by suggesting that AVs offer the first opportunity to rethink urban life and city design since cars replaced horse-powered traffic and changed the design of cities for a hundred years.

A large number of vehicles routinely navigate through city streets; with on-board sensors, they can be transformed into a dynamic network that monitors the urban environment comprehensively and efficiently. In this paper, drive-by approaches are discussed as a form of mobile sensing, that offer a number of advantages over more traditional sensing approaches. It is shown that the physical properties of the urban environment that can be captured using drive-by sensing include Ambient Fluid, Electromagnetic, Urban Envelope, Photonic, and Acoustic properties, which comprise the FEELS classification. In addition, the spatiotemporal variations of these phenomena are discussed as well as their implications on discrete-time sampling. The mobility patterns of sensor-hosting vehicles play a major role in drive-by sensing. Vehicles with scheduled trajectories, e.g., buses, and those with less predictable mobility patterns, e.g., taxis, are investigated for sensing efficacy in terms of spatial and temporal coverage. City Scanner is a drive-by approach with a modular sensing architecture, which enables cost-effective mass data acquisition on a multitude of city features. The City Scanner framework follows a centralized IoT regime to generate a near real-time visualization of sensed data. The sensing platform was mounted on top of garbage trucks and collected drive-by data for eight months in Cambridge, MA, USA. Acquired data were streamed to the cloud for processing and subsequent analyses. Based on a real-world application, we discuss and show the potential of using drive-by approaches to collect environmental data in urban areas using a variety of non-dedicated land vehicles to optimize data collection in terms of spatiotemporal coverage.

Images have played a crucial and enduring role in the study of cities, providing valuable insights into the physical environment and influencing urban design theories. This historical importance ha...