Past Events

PhD Defense

The integration of heterogeneous systems for vehicular sensing


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Tuesday, March 23, 2021, 10:00am - 12:00pm


Speaker: Xiaoyang Xie

Location : Remote via Zoom


Prof. Desheng Zhang (Advisor)

Prof. Zheng Zhang

Prof. Yongfeng Zhang

Prof. Ruilin Liu (external member)

Event Type: PhD Defense

Abstract: With the emergence of connected vehicles and autonomous vehicles in the future, vehicles are being equipped with rich sensing and communication devices, as well as various traffic sensors are being deployed on the road. Based on real-time sensor data from these sensors, tremendous driving assistant applications have been presented to improve urban efficiency, e.g., navigation, HD-live map, and traffic accident recovery, etc. Thus, it is essential to improve vehicular sensing for urban residents. The vehicular sensing could be achieved by modeling mobility patterns of vehicular data from multiple systems, e.g., (1) mobile systems including devices equipped in taxis, buses, trucks, personal vehicles, etc.; and (2) stationary systems including traffic cameras, electronic toll station systems, roadside units, etc. However, most existing works study vehicular sensing on a single system, which may not comprehensively represent the mobility patterns of residents for the whole city, leading to a biased understanding. In this dissertation, we study the integration of multiple vehicular sensing systems and design a vehicular sensing framework, which enables integrated vehicular sensing from three perspectives, i.e.: (1) Integration of mobile systems: we present a real-time sensing task scheduling system, RISC, which schedules sensing tasks for commercial vehicles with constraint sensing resources based on unified modeling for vehicles' mobility patterns; (2) Integration of stationary systems: we design a type-aware travel speed prediction for road segments method, mDrive, by utilizing sparse spatial-temporal data of multiple types of vehicles from traffic camera systems; (3) Integration of hybrid systems: we provide a privacy risk prediction approach based on transfer learning, TransRisk, to predict the privacy risk for a traffic camera system through its small-scale short-term data, and the knowledge learned from large-scale long-term data from existing sensing systems. We implement these three modules of our vehicular sensing framework by real-world data from two Chinese cities, Shenzhen and Suzhou. Our results provide insights for the integration of multiple sensing systems on the improvement of the performance of vehicular sensing from different aspects.


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Meeting ID: 306 256 8848