CarMA: towards personalized automotive tuning

  • Authors:

  • Tobias Flach;Nilesh Mishra;Luis Pedrosa;Christopher Riesz;Ramesh Govindan

  • Affiliations:

  • University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA;Rutgers University, Camden, Camden, NJ;University of Southern California, Los Angeles, CA

  • Venue:
  • Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
  • Year:
  • 2011

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Abstract

Wireless sensing and actuation have been explored in many contexts, but the automotive setting has received relatively little attention. Automobiles have tens of onboard sensors and expose several hundred engine parameters which can be tuned (a form of actuation). The optimal tuning for a vehicle can depend upon terrain, traffic, and road conditions, but the ability to tune a vehicle has only been available to mechanics and enthusiasts. In this paper, we describe the design and implementation of CarMA (Car Mobile Assistant), a system that provides high-level abstractions for sensing automobile parameters and tuning them. Using these abstractions, developers can easily write smart-phone "apps" to achieve fuel efficiency, responsiveness, or safety goals. Users of CarMA can tune their vehicles at the granularity of individual trips, a capability we call personalized tuning. We demonstrate through a variety of applications written on top of CarMA that personalized tuning can result in over 10% gains in fuel efficiency. We achieve this through route-specific or driver-specific customizations. Furthermore, CarMA is capable of improving user satisfaction by increasing responsiveness when necessary, and promoting vehicular safety by appropriately limiting the range of performance available to novice or unsafe drivers.