混合交通流及其他无序流的自驱动粒子模型
Self-Driven Particle Model for Mixed Traffic and Other Disordered Flows
摘要：
发展中国家的车辆尺寸和速度差异很大，司机往往不遵守车道纪律。在这种称为“混合交通”的流动状态下，驾驶员之间的相互作用和由此产生的机动更像是一般的无序自驱动粒子系统，而不是工业化国家的有序车道交通流。我们提出了一个通用的多粒子模型，这种自驱动的“高速粒子”，并表明它再现了所观察到的混合交通的特点。其主要思想是将传统的基于加速度的跟驰模型推广到二维力场。对于直线跟随，模型恢复到底层的汽车跟随模型，对于非常慢的速度，它恢复到行人的各向异性社会力模型。在车道标记位置处添加额外的地板场后，模型恢复为具有连续横向动力学（包括拉链合并等合作方面）的集成车辆跟随和换道模型。以自适应巡航控制（ACC）系统作为底层跟驰模型，它成为自动驾驶车辆在混合交通或基于车道的交通中的加速和转向控制器。
英文摘要：
Vehicles in developing countries have widely varying dimensions and speeds, and drivers tend to not follow lane discipline. In this flow state called "mixed traffic", the interactions between drivers and the resulting maneuvers resemble more that of general disordered self-driven particle systems than that of the orderly lane-based traffic flow of industrialized countries. We propose a general multi particle model for such self-driven "high-speed particles" and show that it reproduces the observed characteristics of mixed traffic. The main idea is to generalize a conventional acceleration-based car-following model to a two-dimensional force field. For in-line following, the model reverts to the underlying car-following model, for very slow speeds, it reverts to an anisotropic social-force model for pedestrians. With additional floor fields at the position of lane markings, the model reverts to an integrated car-following and lane-changing model with continuous lateral dynamics including cooperative aspects such as zip merging. With an adaptive cruise control (ACC) system as underlying car-following model, it becomes a controller for the acceleration and steering of autonomous vehicles in mixed or lane-based traffic.
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