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Multi-Task-Oriented Vehicular Crowdsensing: A Deep Learning Approach

机译：面向多任务的车辆人群感知：一种深度学习方法

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摘要

With the popularity of drones and driverless cars, vehicular crowdsensing (VCS) becomes increasingly widely-used by taking advantage of their high-precision sensors and durability in harsh environments. Since abrupt sensing tasks usually cannot be prepared beforehand, we need a generic control logic fit-for-use all tasks which are similar in nature, but different in their own settings like Point-of-Interest (PoI) distributions. The objectives include to simultaneously maximize the data collection amount, geographic fairness, and minimize the energy consumption of all vehicles for all tasks, which usually cannot be explicitly expressed in a closed-form equation, thus not tractable as an optimization problem. In this paper, we propose a deep reinforcement learning (DRL)-based centralized control, distributed execution framework for multi-task-oriented VCS, called "DRL-MTVCS". It includes an asynchronous architecture with spatiotemporal state information modeling, multi-task-oriented value estimates by adaptive normalization, and auxiliary vehicle action exploration by pixel control. We compare with three baselines, and results show that DRL-MTVCS outperforms all others in terms of energy efficiency when varying different numbers of tasks, vehicles, charging stations and sensing range.
机译：随着无人驾驶汽车和无人驾驶汽车的普及，车辆拥挤感测（VCS）凭借其高精度传感器和在恶劣环境下的耐用性而变得越来越广泛。由于通常无法事先准备突然的感知任务，因此我们需要适合所有用途的通用控制逻辑，这些任务本质上是相似的，但其自身设置（例如兴趣点（PoI）分布）不同。目标包括同时最大化数据收集量，地理公平性和最小化所有车辆用于所有任务的能源消耗，这通常不能以封闭形式的方程式明确表示，因此很难作为优化问题解决。在本文中，我们提出了一种基于深度强化学习（DRL）的集中控制，面向多任务的VCS的分布式执行框架，称为“ DRL-MTVCS”。它包括具有时空状态信息建模的异步体系结构，通过自适应归一化进行的面向多任务的值估计以及通过像素控制进行的辅助车辆动作探索。我们与三个基准进行比较，结果表明，在改变不同数量的任务，车辆，充电站和感应范围时，DRL-MTVCS在能源效率方面优于所有其他基准。

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