As part of KGUN9's continuing project "Operation Safe Roads" reporters looked at how college students are learning to program cars that will probably be in your future. In a parking lot, a car, and the college students running it, are building the skills to move self-driving cars from an experiment to an everyday appliance you'd trust with your life.  They're engineering students from a long list of universities gathered at University of Arizona for a special summer program through the National Science Foundation. University of Alabama student Landon Bentley remembers when it was an achievement just to program a car to stay in a straight line.  “I think when a car can merge onto the Interstate, go through stop and go traffic and come back out without having any human interaction I think that would be quite the feat. We can do some of those steps individually. But I think when that whole process is streamlined that, we'll be there." Read more ...

TUCSON - Students from across the U.S. are putting their skills to the test, as they remotely drive the University of Arizona's Cognitive and Autonomous Test (CAT) vehicle.  The students are taking part in the National Science Foundation's Research Experiences for Undergraduates Program. This initiative offers students from diverse backgrounds valuable research opportunities at major U.S. universities. "They work on the autonomous vehicle and they work on safety issues of the vehicle.

Sterling Holcomb, Audrey Knowlton, and Juan Guerra published results of their research project in WinnComm'16, the Wireless Innovation Forum Conference on Wireless Communications Technologies and Software Defined Radio, in Reston, VA. Their paper, titled "Power Efficient Vehicular Ad Hoc Networks" was a new approach for Vehicular AdHoc Networks (VANETS) that represents a significant reduction in power use. This benefit enables more vehicles to communicate simultaneously. Through simulation, students showed that their approach to VANETs maintains its update frequency despite bumper to bumper traffic and uses two to five orders of magnitude less power than an IEEE 802.11 network with clustering and 1 mW transmit power. Overall, the network performed well and is a viable improvement to the standard. 

The paper was presented in the technical track, "Top 10 Most Wanted Wireless Innovations."

The bibliographic information for the paper is below, or can be accessed at

S.  Holcomb, Knowlton, A., Guerra, J., Asadi, H., Volos, H., Sprinkle, J., and Bose, T., "Power Efficient Vehicular Ad Hoc Networks", WInnComm. Reston, VA, pp. 26-31, 2016.

Yegeta Zeleke and Kennon McKeever published results of their research project on Constraint-Based Modeling of Autonomous Vehicle Trajectories in the 15th Workshop on Domain-Specific Modeling, which is the longest-running workshop in the history of SPLASH/OOPSLA. Their paper, titled "Experience Report: Constraint-based Modeling of Autonomous Vehicle Trajectories" focused on enabling young students (e.g., in elementary school) to safely control a dangerous robot, such as autonomous car, through the application of constraint-based checks during the code generation process. Their experience report discussed the creation of a domain-specific language that allows for faster programming of autonomous vehicles while ensuring valid constraints will be met. The language generates code for multiple controllers that will operate alternatively to allow for fast and effective programming of vehicle trajectories using primitive motions. In addition to improving coding efficiency and reducing the number of programming errors, the language adds a level of abstraction so that autonomous vehicle behaviors may be generated by people with little knowledge of low-level details of the car’s operation. Furthermore, this language ensures safe operation of the vehicle by enforcing a set of user-definable constraints on the output path. A main set of constraints that are applied to every generated path have been specifically chosen to enforce safe switching between controllers and prevent the planning of unsafe actions. A novel application of the language is its ability to permit users to add specific constraints for a particular path; these constraints are checked for validity after the main constraint check is performed.