It has been demonstrated that leading cruise control (LCC) can improve the operation of mixed-autonomy platoons by allowing connected and automated vehicles (CAVs) to make longitudinal control decisions based on the information provided by surrounding vehicles. However, LCC generally requires surrounding human-driven vehicles (HDVs) to share their real-time states, which can be used by adversaries to infer drivers' car-following behavior, potentially leading to financial losses or safety concerns. This paper aims to address such privacy concerns and protect the behavioral characteristics of HDVs by devising a parameter privacy-preserving approach for mixed-autonomy platoon control. First, we integrate a parameter privacy filter into LCC to protect sensitive car-following parameters. The privacy filter allows each vehicle to generate seemingly realistic pseudo states by distorting the true parameters to pseudo parameters, which can protect drivers' privacy in behavioral parameters without significantly influencing the control performance. Second, to enhance the practicality and reliability of the privacy filter within LCC, we first extend the current approach to accommodate continuous parameter spaces through a neural network estimator. Subsequently, we introduce an individual-level parameter privacy preservation constraint, focusing on the privacy level of each individual parameter pair, further enhancing the approach's reliability. Third, analysis of head-to-tail string stability reveals the potential impact of privacy filters in degrading mixed traffic flow performance. Simulation shows that this approach can effectively trade off privacy and control performance in LCC. We further demonstrate the benefit of such an approach in networked systems, i.e., by applying the privacy filter to a proceeding vehicle, one can also achieve a certain level of privacy for the following vehicle.

翻译：研究表明，领航巡航控制（LCC）通过允许网联自动驾驶车辆（CAV）基于周围车辆信息做出纵向控制决策，可有效提升混合自主车队的运行效率。然而，LCC通常要求周围的人类驾驶车辆（HDV）共享其实时状态，这些数据可能被攻击者利用以推断驾驶员的跟车行为特征，进而引发经济损失或安全隐患。本文旨在通过设计混合自主车队控制的参数隐私保护方法，解决此类隐私问题并保护HDV的行为特征。首先，我们在LCC中集成参数隐私过滤器以保护敏感的跟车参数。该过滤器通过将真实参数扭曲为伪参数，使每辆车生成看似真实的伪状态，从而在不显著影响控制性能的前提下保护驾驶员行为参数的隐私。其次，为增强LCC中隐私过滤器的实用性与可靠性，我们通过神经网络估计器将现有方法扩展至连续参数空间。随后引入个体级参数隐私保护约束，聚焦于每个独立参数对的隐私保护级别，进一步提升方法的可靠性。第三，对头尾串稳定性分析揭示了隐私过滤器可能降低混合交通流性能的潜在影响。仿真表明，该方法能有效平衡LCC的隐私保护与控制性能。我们进一步展示了该方法在网络系统中的优势：通过将隐私过滤器应用于前车，后续车辆同样可获得一定程度的隐私保护。