| Abstract |
With the rapid development of intelligent vehicular technologies, such as Self-driving systems and Advanced Driver Assistance systems, off-the-shelf intelligent vehicles are equipped with more and more sensors, including GPS, camera, Lidar, etc., thus possessing powerful capabilities of computation and communication along with large-scale storage capacity. As an important kind of the intelligent vehicle, the Mobility-On-Demand(MOD) vehicles (such as Uber, DiDi, and connected taxis) have large-scale, fine-grained coverage in cities along with non-negligible amounts of spare time. Hence, utilizing their available sensors provides promising opportunities in achieving large-scale, fine-grained, and low-cost vehicular crowdsensing for smart cities. As a result, this paper focuses on these MOD vehicles and studies how to optimally allocate the vehicular crowdsensing tasks for the MOD vehicles. It chiefly involves two main challenges: (1)Both the distributions of the MOD vehicles and the sensing tasks have spatial-temporal differences. Also, the pick-up earnings of MOD vehicles vary with the location and time. Hence, it renders the sensing cost highly dynamic in both temporal and spatial dimensions. Even worse, such sensing cost is hard to model because of its highly dynamic nature. (2)The optimal sensing task allocation is a NP-hard problem, which has exponential time complexity. Furthermore, owing to the high mobility of the vehicles, it requires real-time task allocation in vehicular crowdsensing. To address these challenges, we propose a deep reinforcement learning-empowered near-optimal task allocation method for vehicular crowdsensing. We utilize deep reinforcement learning to extract the highly dynamic sensing cost of vehicles, which is fed back to optimally allocate the sensing tasks for each MOD vehicle. Specifically, targeting the first challenge, we deploy the Encoder-Decoder Recurrent Neural Network based on dual attentions (including the spatial attention and the temporal attention) to extract the spatial-temporal correlations of pick-up earnings, which are then used to learn the sensing cost according to the driving cost model. Furthermore, through the equivalent problem transformation, we prove that the task allocation problem has a submodular objective function and a q-dependent constraint. Hence, based on the sub-modularity theory, we propose a near-optimal task allocation algorithm, jointly considering the total utility and marginal utility. It is proved to achieve a 1/ 2+cmax/cmin -approximation ratio in polynomial time, where cmax and cmin represent the maximal and minimal values of the sensing costs for all the vehicles, respectively. Finally, we exploit two large-scale datasets to evaluate the performance of the proposed method. One dataset is about 12493 MOD vehicles in Chongqing City, China, while the other is about 113 million vehicle trips in New York City, America. The results demonstrate that our method averagely improves the prediction accuracy of pick-up earnings and the allocation utility of sensing tasks by 25.1% and 37.7%, respectively, compared with seven baselines. Moreover, we implement a prototype system for on-road illegal parking detection, i.e., leveraging the smartphone sensor (such as camera and GPS) of massive MOD vehicles to detect the on-road illegal parking events when driving on roads. Based on this system, we validate the proposed method is feasible and significant in practical applications. © 2022, Science Press. All right reserved. |
