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F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds
Autonomous vehicles are heavily reliant upon their sensors to perfect th...
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Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles
Sensor-based perception on vehicles are becoming prevalent and important...
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Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems
The vehicular edge computing (VEC) system integrates the computing resou...
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Multi-objects association in perception of dynamical situation
In current perception systems applied to the rebuilding of the environme...
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Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving
Autonomous vehicles (AVs) can achieve the desired results within a short...
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Lessons Learned from Accident of Autonomous Vehicle Testing: An Edge Learning-aided Offloading Framework
This letter proposes an edge learning-based offloading framework for aut...
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iRDRC: An Intelligent Real-time Dual-functional Radar-Communication System for Automotive Vehicles
This letter introduces an intelligent Real-time Dual-functional Radar-Co...
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LiveMap: Real-Time Dynamic Map in Automotive Edge Computing
Autonomous driving needs various line-of-sight sensors to perceive surroundings that could be impaired under diverse environment uncertainties such as visual occlusion and extreme weather. To improve driving safety, we explore to wirelessly share perception information among connected vehicles within automotive edge computing networks. Sharing massive perception data in real time, however, is challenging under dynamic networking conditions and varying computation workloads. In this paper, we propose LiveMap, a real-time dynamic map, that detects, matches, and tracks objects on the road with crowdsourcing data from connected vehicles in sub-second. We develop the data plane of LiveMap that efficiently processes individual vehicle data with object detection, projection, feature extraction, object matching, and effectively integrates objects from multiple vehicles with object combination. We design the control plane of LiveMap that allows adaptive offloading of vehicle computations, and develop an intelligent vehicle scheduling and offloading algorithm to reduce the offloading latency of vehicles based on deep reinforcement learning (DRL) techniques. We implement LiveMap on a small-scale testbed and develop a large-scale network simulator. We evaluate the performance of LiveMap with both experiments and simulations, and the results show LiveMap reduces 34.1
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