Environmental-Based Speed Recommendation for Future Smart Cars

Ioannis Galanis, Iraklis Anagnostopoulos, Priyaa Gurunathan and Dona Burkard
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Ioannis Galanis: Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL 62901, USA
Iraklis Anagnostopoulos: Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL 62901, USA
Priyaa Gurunathan: Research & Innovation Center, Ford Motor Company, Dearborn, MI 48124, USA
Dona Burkard: Research & Innovation Center, Ford Motor Company, Dearborn, MI 48124, USA

Future Internet, 2019, vol. 11, issue 3, 1-18

Abstract: Modern vehicles are enhanced with increased computation, communication and sensing capabilities, providing a variety of new features that pave the way for the deployment of more sophisticated services. Specifically, smart cars employ hundreds of sensors and electronic systems in order to obtain situational and environmental information. This rapid growth of on-vehicle multi-sensor inputs along with off-vehicle data streams introduce the smart car era. Thus, systematic techniques for combining information provided by on- and off-vehicle car connectivity are of remarkable importance for the availability and robustness of the overall system. This paper presents a new method to employ service oriented agents that cohesively align on- and off-vehicle information in order to estimate the current status of the car. In particular, this work combines, integrates, and evaluates multiple information sources targeting future smart cars. Specifically, the proposed methodology leverages weather-based, on-route, and on-vehicle information. As a use case, the presented work informs the driver about the recommended speed that the car should adapt to, based on the current status of the car. It also validates the proposed speed with real-time vehicular measurements.

Keywords: smart cars; environmental information; speed recommendation; points of interest (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
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