Modern driver assistance systems as well as autonomous vehicles take their decisions based on local maps of the environment. These maps include, for example, surrounding moving objects perceived by sensors as well as routes and navigation information. Current research in the field of environment mapping is concerned with two major challenges. The first one is the integration of information from different sources e.g. on-board sensors like radar, camera, ultrasound and lidar, offline map data or backend information. The second challenge comprises in finding an abstract representation of this aggregated information with suitable interfaces for different driving functions and traffic situations. To overcome these challenges, an extended environment model is a reasonable choice. In this paper, we show that role-based motion predictions in combination with v2x-extended environment models are able to contribute to increased traffic safety and driving comfort. Thus, we combine the mentioned research areas and show possible improvements, using the example of a threading process at a motorway access road. Furthermore, it is shown that already an average v2x equipment penetration of 80% can lead to a significant improvement of 0.33m/s^2 of the total acceleration and 12m more safety distance compared to non v2x-equipped vehicles during the threading process.

中文翻译：

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使用通过 V2X 通信和基于角色的行为预测扩展的环境模型为安全和舒适的匝道合并提供驾驶员辅助

现代驾驶员辅助系统以及自动驾驶汽车根据当地的环境地图做出决定。例如，这些地图包括传感器感知的周围移动物体以及路线和导航信息。当前环境测绘领域的研究涉及两个主要挑战。第一个是整合来自不同来源的信息，例如雷达、相机、超声波和激光雷达等车载传感器、离线地图数据或后端信息。第二个挑战包括找到具有适用于不同驾驶功能和交通情况的合适接口的聚合信息的抽象表示。为了克服这些挑战，扩展环境模型是一个合理的选择。在本文中，我们表明，基于角色的运动预测与 v2x 扩展环境模型相结合能够有助于提高交通安全和驾驶舒适度。因此，我们结合上述研究领域并以高速公路通道的穿线过程为例展示了可能的改进。此外，研究表明，在穿线过程中，与未配备 v2x 的车辆相比，平均 80% 的 v2x 设备渗透率可以使总加速度显着提高 0.33m/s^2，安全距离增加 12m。