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Enabling Smart Driving through Sensing and Communication in Vehicular Networks
Li, Dong

2014, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
An increasing number of vehicles are getting equipped with radios and sensing devices. The radios enable Inter-Vehicle-Communication (IVC), which can broaden the vision of the drivers and extend the ability of the sensing devices. However, implementing many collaborative applications in vehicular networks, such as autonomous cruise control, collaborative driving, and pre-crash sensing, needs additional services such as 1) service discovery when there are limited contact opportunities; 2) relative localization of neighboring vehicles; 3) communication addresses of the vehicles in vision; and 4) collaboration with neighboring vehicles. In this dissertation, we propose the concept of smart driving with the goal of providing the fundamental services for the real world cooperative vehicular applications based on IVC. This dissertation presents four schemes towards achieving the goal of achieving smart driving: RBTP, MARVEL, ForeSight and RoadView.

RBTP allows radios to quickly discover each other in a mobile environment. RBTP is a scheme that determines how the devices should wake up and sleep to achieve minimal contact latency with other nearby devices. RBTP achieves provable performance bound and outperforms state-of-the-art asynchronous protocols for mobile devices. When compared with the optimum scheme, the contact latency is shown to be within a factor of 9/8 in the expected case and 2 in the worst case.

MARVEL is a system by which a vehicle can identify the relative lane positions of the neighboring vehicles. Access to relative locations 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. MARVEL is a novel antenna diversity based solution. Unlike existing technologies such as camera and radar, MARVEL can also determine the relative locations 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.

ForeSight is a system that identifies the communication addresses of neighboring vehicles. We observed that the on-board camera and sensors can observe different features of the vehicle itself and some of the neighboring vehicles. The vehicle can use the radio to broadcast its features to other vehicles. ForeSight takes advantage of the diversity of the features of different vehicles, to match the vehicles observed through the camera and the information received over the radio. ForeSight is designed to work robustly in presence of legacy vehicles.

Finally, the dissertation discusses RoadView, which can collaboratively create the global view of all vehicles based on their local detection results. The global view faciliates cooperative applications between the adopted vehicles by providing the relative location of the vehicles and the global identities of the adopted vehicles. RoadView allows a participating vehicle to detect vehicles in the regions that are not covered by its sensors. RoadView has low hardware requirement and is designed to work in low adoption rate scenarios.
Prasun Sinha (Advisor)
Kannan Srinivasan (Committee Member)
Dong Xuan (Committee Member)
160 p.

Recommended Citations

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Li, D. (2014). Enabling Smart Driving through Sensing and Communication in Vehicular Networks. (Electronic Thesis or Dissertation). Retrieved from

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Li, Dong. "Enabling Smart Driving through Sensing and Communication in Vehicular Networks." Electronic Thesis or Dissertation. Ohio State University, 2014. OhioLINK Electronic Theses and Dissertations Center. 23 Sep 2018.

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Li, Dong "Enabling Smart Driving through Sensing and Communication in Vehicular Networks." Electronic Thesis or Dissertation. Ohio State University, 2014.


Full text release has been delayed at the author's request until September 18, 2019