The high-definition (HD) map is a cornerstone of autonomous driving. The crowdsourcing paradigm is a cost-effective way to keep an HD map up-to-date. Current HD map crowdsourcing mechanisms aim to enhance HD map freshness within recruitment budgets. However, many overlook unique and critical traits of crowdsourcing vehicles, such as random arrival and heterogeneity, leading to either compromised map freshness or excessive recruitment costs. Furthermore, these characteristics complicate the characterization of the feasible space of the optimal recruitment policy, necessitating a method to compute it efficiently in dynamic transportation scenarios.To overcome these challenges, we propose an efficient and cost-effective vehicle recruitment (ENTER) mechanism. Specifically, the ENTER mechanism has a threshold structure and balances freshness with recruitment costs while accounting for the vehicles' random arrival and heterogeneity. It also integrates the bound-based relative value iteration (RVI) algorithm, which utilizes the threshold-type structure and upper bounds of thresholds to reduce the feasible space and expedite convergence. Numerical results show that the proposed ENTER mechanism increases the HD map company's payoff by 23.40% and 43.91% compared to state-of-the-art mechanisms that do not account for vehicle heterogeneity and random arrivals, respectively. Furthermore, the bound-based RVI algorithm in the ENTER mechanism reduces computation time by an average of 18.91% compared to the leading RVI-based algorithm.

翻译：高清地图是自动驾驶的基石。众包模式是一种保持高清地图更新的经济有效方式。当前的高清地图众包机制旨在招募预算内提升地图新鲜度。然而，许多机制忽略了众包车辆的独特与关键特性，例如随机到达和异质性，导致地图新鲜度受损或招募成本过高。此外，这些特性使得最优招募策略可行空间的表征变得复杂，需要一种能在动态交通场景中高效计算该空间的方法。为应对这些挑战，我们提出了一种高效且经济的车辆招募（ENTER）机制。具体而言，ENTER机制具有阈值结构，在平衡新鲜度与招募成本的同时，考虑了车辆的随机到达和异质性。该机制还集成了基于边界的相对值迭代（RVI）算法，利用阈值型结构和阈值上界来缩小可行空间并加速收敛。数值结果表明，与未考虑车辆异质性和随机到达的最先进机制相比，所提出的ENTER机制分别使高清地图公司收益提升23.40%和43.91%。此外，ENTER机制中基于边界的RVI算法相较于领先的基于RVI的算法，平均计算时间减少18.91%。