A Platform-Based Incentive Mechanism for Autonomous Vehicle Crowdsensing
A Platform-Based Incentive Mechanism for Autonomous Vehicle Crowdsensing
复制标题
DOI：
10.1109/ojits.2021.3056925
复制
发表时间：
2021
影响因子：
2.6
通讯作者：
Alireza Chakeri;Xin Wang;Quentin Goss;M. Akbaş;L. Jaimes
中科院分区：
文献类型：
--
作者：
Alireza Chakeri;Xin Wang;Quentin Goss;M. Akbaş;L. Jaimes
在这篇文章中，我们提出了一个激励机制，车辆人群感知的背景下，自动驾驶汽车（AV）。特别是，我们提出了一个解决方案的问题，感测覆盖的区域位于AV的计划轨迹。我们通过动态修改AV的轨迹并从最初计划的路线无法到达的区域收集感测样本来解决这个问题。我们将这个问题建模为一个非合作博弈，其中一组配备传感器的无人驾驶汽车是玩家，它们的轨迹是策略。因此，我们的解决方案对应于一个模型，在该模型中，预期的个人效用驱动参与者的移动决策。使用开放的街道地图，SUMO车辆交通模拟器，和广泛的模拟，我们表明我们的算法显着优于传统的轨迹生成方法。特别是，我们的绩效评估显示，众包覆盖率，道路利用率和平均参与者效用显着提升。
In this article, we present an incentive mechanism for Vehicular Crowdsensing in the context of autonomous vehicles (AVs). In particular, we propose a solution to the problem of sensing coverage of regions located out of the AVs’ planned trajectories. We tackle this problem by dynamically modifying the AVs’ trajectories and collecting sensing samples from regions otherwise unreachable by originally planned routes. We model this problem as a non-cooperative game in which a set of AVs equipped with sensors are the players and their trajectories are the strategies. Thus, our solution corresponds to a model in which expected individual utility drives the mobility decision of participants. Using open-street maps, SUMO vehicular traffic simulator, and extensive simulations, we show our algorithm significantly outperforms traditional approaches for trajectory generation. In particular, our performance evaluation shows a significant lift in crowdsourcer coverage, road utilization, and average participant utility.