The high-definition map is a cornerstone of autonomous driving. Unlike constructing a costly fleet of mapping vehicles, the crowdsourcing paradigm is a cost-effective way to keep an HD map up to date. Achieving practical success for crowdsourcing-based HD maps is contingent on addressing two critical issues: freshness and recruitment costs. Given that crowdsource vehicles are often heterogeneous in terms of operational costs and sensing capabilities, it is practical to recruit heterogeneous crowdsource vehicles to achieve the tradeoff between freshness and recruitment costs. However, existing works neglect this aspect. To solve it, we formulate this problem as a Markov decision process. We demonstrate that the optimal policy is threshold-type age-dependent. Additionally, our findings reveal some counter-intuitive insights. In some cases, the company should initiate vehicle recruitment earlier when vehicles arrive more frequently, or have higher operational costs or sensing capabilities.} Besides, we propose an efficient algorithm, called the bound-based relative value iteration (BRVI) algorithm, to overcome the technical challenge that finding an optimal policy is time-consuming. Numerical simulations show that (i) the optimal policy reduces the average cost by $19.04\%$ compared to the state-of-the-art mechanism}, and (ii) the proposed algorithm can reduce the convergence time by $13.66\%$ on average compared to the existing algorithm.

翻译：高清地图是自动驾驶的基石。与建设昂贵的测绘车队不同，众包范式是一种保持高清地图更新的经济有效方式。实现基于众包的高清地图的实际成功取决于解决两个关键问题：新鲜度和招募成本。鉴于众包车辆在运营成本和感知能力方面通常是异构的，因此招募异构众包车辆以实现新鲜度与招募成本之间的权衡是切实可行的。然而，现有工作忽视了这一点。为解决该问题，我们将其建模为马尔可夫决策过程。我们证明最优策略是阈值型、与年龄相关的。此外，我们的发现揭示了一些反直觉的见解。在某些情况下，当车辆到达更频繁，或具有更高运营成本或感知能力时，公司应更早启动车辆招募。此外，我们提出了一种高效算法，称为基于边界的相对值迭代（BRVI）算法，以克服寻找最优策略耗时这一技术挑战。数值仿真表明：（i）与现有最优机制相比，最优策略平均成本降低19.04%；（ii）与现有算法相比，所提算法平均收敛时间减少13.66%。