Cooperative navigation (CN) is a widespread technique to have efficient navigation of intelligent vehicles. Nonetheless, the CN strategies need to be more consistent in estimating and managing in-road risks. This paper outlines a flexible CN scheme for multiple unmanned ground vehicles (MUGVs) system to deal with such critical cooperative system. With its relative low execution time, the probability collectives (PC) algorithm has succeeded at generating fast and feasible solutions to cross intersections and roundabouts (Philippe et al. 1928–1934, 2019). However, the PC is still sensitive to uncertainty in the navigation process, which highlights the need to adopt several safety margins. This work focuses on balancing between the high-quality cooperative optimization and acceptable computational speed. Thus, a reliable risk management strategy is proposed by introducing a novel ε-constraint PC method. A real-time communication mechanism is suggested for a distributed system to avoid invalid behavior due to inconsistency. The novel ε-PC based navigation strategy allows the vehicles to adapt their dynamics and react to unexpected events while respecting real-time constraints. One finding appears to be well substantiated by the typical common-yet-difficult scenarios in intensive simulations. The \(\varepsilon\)-PC method can ensure collision-free behaviors and reserve at least 1.5s of reaction time for vehicles’ safety insurance.

合作导航（CN）是一种广泛的技术，可以有效地导航智能车辆。但是，CN的策略在估计和管理道路风险时需要更加一致。本文概述了一种灵活的CN方案，用于处理多个关键协同系统的多个无人地面飞行器（MUGV）系统。由于执行时间相对较短，概率集合算法（PC）已成功生成了快速，可行的十字路口和回旋处解决方案（Philippe等人，1928年至1934年，2019年）。但是，PC机仍然对导航过程中的不确定性敏感，这突出表明需要采用多个安全裕度。这项工作的重点是在高质量的协作优化和可接受的计算速度之间取得平衡。因此，ε-约束PC法。建议为分布式系统提供一种实时通信机制，以避免由于不一致而导致的无效行为。新颖的基于ε- PC的导航策略使车辆在遵守实时约束的同时，能够适应动态变化并对突发事件做出反应。在密集模拟中，典型的常见但困难的场景似乎充分证明了这一发现。该\（\ varepsilon \） -PC方法可以在车辆的安全保险的反应时间至少1.5秒确保无冲突的行为和储备。