Platooning or cooperative adaptive cruise control (CACC) has been investigated for decades, but debate about its lasting impact is still ongoing. Even though platooning benefits and platoon formation are rather well understood for trucks, this is less clear for passenger cars, which have a higher heterogeneity in trips and drivers' preferences. Most importantly, it remains unclear how to form platoons of passenger cars in order to optimize the personal benefit for the individual driver. To this end, in this paper, we propose a novel platoon formation algorithm that optimizes the personal benefit for drivers of individual passenger cars. For computing vehicle-to-platoon assignments, the algorithm utilizes a new metric that we propose to evaluate the personal benefits of various driving systems, including platooning. By combining fuel and travel time costs into a single monetary value, drivers can estimate overall trip costs according to a personal monetary value for time spent. This provides an intuitive way for drivers to understand and compare the benefits of driving systems like human driving, adaptive cruise control (ACC), and, of course, platooning. Unlike previous similarity-based methods, our proposed algorithm forms platoons only when beneficial for the driver, rather than for the sake of platooning only. Results of a large-scale simulation study demonstrate that our proposed algorithm outperforms normal ACC as well as previous similarity-based platooning approaches by balancing fuel savings and travel time, independent of traffic and drivers' time cost.

翻译：队列行驶或协同自适应巡航控制（CACC）已被研究数十年，但其长期影响仍存争议。尽管卡车队列行驶的益处及队列形成机制已较为明确，但对于行程和驾驶员偏好异质性更高的乘用车而言，情况则不甚清晰。最关键的是，如何形成乘用车队列以优化个体驾驶员的个人收益仍不明确。为此，本文提出一种新颖的队列形成算法，旨在优化个体乘用车驾驶员的个人收益。该算法采用我们提出的新度量标准来计算车辆与队列的匹配关系，以评估包括队列行驶在内的各种驾驶系统的个人收益。通过将燃油消耗与行程时间成本合并为单一货币价值，驾驶员可根据个人时间价值估算整体出行成本。这为驾驶员提供了一种直观的方式来理解和比较人工驾驶、自适应巡航控制（ACC）以及队列行驶等驾驶系统的收益。与先前基于相似性的方法不同，我们提出的算法仅在有利于驾驶员时才会形成队列，而非仅为队列行驶而组建。大规模仿真研究结果表明，所提算法通过平衡燃油节约与行程时间，在不受交通状况和驾驶员时间成本影响的情况下，其性能优于常规ACC及以往基于相似性的队列行驶方法。