论文标题
机器学习在轨迹预测中的作用在合作驾驶中
The Role of Machine Learning for Trajectory Prediction in Cooperative Driving
论文作者
论文摘要
在本文中，我们研究了机器学习在合作驾驶中可以扮演的角色。鉴于现代车辆和道路基础设施的连通性速度提高，合作驾驶是自动驾驶的有希望的第一步。我们在本文中探索的示例场景是协调的车道合并，数据收集，测试和评估均在汽车测试轨道中进行。假设是车辆是配备通信单元的车辆的混合，即连接的车辆和未连接的车辆。但是，路边摄像头已连接，可以捕获所有车辆，包括没有连接的车辆。我们开发了一个交通编排者，该协调员建议基于这两个信息来源，即连接的车辆和连接的路边摄像机，建议轨迹。建立了推荐的轨迹，然后将其传达回连接的车辆。我们探讨了对轨迹的准确和及时预测的不同机器学习技术的使用。
In this paper, we study the role that machine learning can play in cooperative driving. Given the increasing rate of connectivity in modern vehicles, and road infrastructure, cooperative driving is a promising first step in automated driving. The example scenario we explored in this paper, is coordinated lane merge, with data collection, test and evaluation all conducted in an automotive test track. The assumption is that vehicles are a mix of those equipped with communication units on board, i.e. connected vehicles, and those that are not connected. However, roadside cameras are connected and can capture all vehicles including those without connectivity. We develop a Traffic Orchestrator that suggests trajectories based on these two sources of information, i.e. connected vehicles, and connected roadside cameras. Recommended trajectories are built, which are then communicated back to the connected vehicles. We explore the use of different machine learning techniques in accurately and timely prediction of trajectories.
