Towards Active Machine-Vision-Based Driver Assistance for Urban Areas
International Journal of Computer Vision
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Practical asynchronous neighbor discovery and rendezvous for mobile sensing applications
Proceedings of the 6th ACM conference on Embedded network sensor systems
Nericell: using mobile smartphones for rich monitoring of road and traffic conditions
Proceedings of the 6th ACM conference on Embedded network sensor systems
U-connect: a low-latency energy-efficient asynchronous neighbor discovery protocol
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Detecting driver phone use leveraging car speakers
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
Lane Detection With Moving Vehicles in the Traffic Scenes
IEEE Transactions on Intelligent Transportation Systems
MARVEL: multiple antenna based relative vehicle localizer
Proceedings of the 18th annual international conference on Mobile computing and networking
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Access to relative location of nearby vehicles on the local roads or on the freeways is useful for providing critical alerts to the drivers, thereby enhancing their driving experience as well as reducing the chances of accidents. The problem of determining the relative location of two vehicles can be broken into two smaller subproblems: (i) Relative lane localization, where a vehicle determines if the other vehicle is in left lane, same lane or right lane with respect to it, and (ii) Relative front-rear localization where it needs to be determined which of the two vehicles is ahead of the other on the road. In this paper, we propose a novel antenna diversity based solution, MARVEL, that solves the two problems of determining the relative location of two vehicles. MARVEL has two components: (i) a smartphone; and (ii) four wireless radios. Unlike exisiting technologies, MARVEL can also determine relative location of vehicles that are not in the immediate neighborhood, thereby providing the driver with more time to react. Further, due to minimal hardware requirements, the deployment cost of MARVEL is low and it can be easily installed on newer as well as existing vehicles. Using results from our real driving tests, we show that MARVEL is able to determine the relative lane location of two vehicles with 96.8% accuracy. Through trace-driven simulations, we also show that by aggregating information across different vehicles, MARVEL is able to increase the localization accuracy to 98%.