Within the next few years, autonomous vehicles will start being commercialized. In the same time, inter-vehicular communication is emerging. Using these new technologies allows designing new software to make cooperative cars. These cooperatives cars can exchange messages to improve traffic efficiency by, for instance, notifying about the presences of other cars, or managing the right-of-way at intersections. In the context of multi-lane roads, we propose to use the communication between vehicles to design a cooperative intelligence, based on evolutionary optimization where the behavior of each vehicle is regularly updated according to the behavior of surrounding vehicles and a fitness function. This paper presents an automated/cooperative lane-change framework where the parameters of the system are dynamically adjusted using an online evolutionary algorithm. The goal is to make the cars adjust their behavior according to the local traffic conditions. Simulations are carried out showing a performance improvement in terms of traffic fluidity.

中文翻译：

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基于 V2V 的模因优化提高多车道道路交通效率

在未来几年内，自动驾驶汽车将开始商业化。与此同时，车辆间通信正在兴起。使用这些新技术可以设计新软件来制造合作汽车。这些合作汽车可以交换信息以提高交通效率，例如，通知其他汽车的存在，或管理十字路口的通行权。在多车道道路的背景下，我们建议使用车辆之间的通信来设计基于进化优化的协作智能，其中每个车辆的行为根据周围车辆的行为和适应度函数定期更新。本文提出了一种自动/协作换道框架，其中使用在线进化算法动态调整系统参数。目标是让汽车根据当地的交通状况调整自己的行为。进行的模拟显示了交通流动性方面的性能改进。