Candidate: Ruoxu Wang
Date: August 1, 2024
Time: 10:30am
Location: https://uwaterloo.zoom.us/j/94053906869?pwd=IRWo9Z99OEaZBlB7RNUwJblckKnCWn.1
Supervisor: Weihua Zhuang
All are welcome!
Abstract:
Vehicular see-through service is one of the driver assistance services expected to be provided by smart vehicles in the future. It is envisioned that this service will expand the vision of a smart-vehicle driver by timely alerting the driver to obstacles in the front blind spots. In this seminar, we investigate cooperative perception and resource allocation in the provision of the vehicular see-through service to ensure that drivers are able to obtain accurate and timely environment information about their front blind spots. To cope with the consumption of communication and computing resources associated with the transmission and processing of multi-view point clouds, we propose a cooperative sensor data collection and processing scheme. In this scheme, smart vehicles cooperatively collect point cloud data for each object and complete the point cloud processing at the on-board computing unit. For each object, we select a set of collectors to collect the point cloud data and an analyzer to process the data. To address the tradeoff between classification accuracy and resource efficiency, we investigate the impact of the location of cooperative vehicles on object classification accuracy and propose an indicator to assess the quality of the collector sets. We develop a collector set pre-selection algorithm that identifies all collector sets for each object, to satisfy the classification accuracy and have minimal data redundancy. To address the tradeoff between the service requirements and the overall resource consumption of the system, we treat each potential collector set and its analyzer of an object as a worker pair and select the worker pair for each object based on its resource consumption. Taking into account the resource competition among worker pairs, we develop a joint vehicle selection and resource allocation algorithm based on ant colony optimization to minimize the overall resource consumption, while satisfying the delay requirements of the service. Simulation results demonstrate that our proposed service provisioning scheme outperforms benchmark schemes in terms of resource efficiency, task completion rate and request completion rate.