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.

中文翻译：

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基于平台的自动驾驶人群激励机制

在本文中，我们提出了一种在自动驾驶汽车（AVs）背景下的车辆拥挤激励机制。尤其是，我们提出了一种解决方案，用于感测位于AV计划轨迹之外的区域的覆盖范围。我们通过动态修改AV的轨迹并从原先计划的路线无法到达的区域收集传感样本来解决此问题。我们将此问题建模为非合作游戏，其中，一组配备传感器的AV充当玩家，其轨迹作为策略。因此，我们的解决方案对应于一个模型，在模型中，预期的个人效用将驱动参与者的流动性决策。使用开放街道地图，SUMO车辆交通模拟器以及广泛的模拟，我们证明了我们的算法明显优于传统的轨迹生成方法。尤其是，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高。