Tengg Allan, Stolz Michael, Hillebrand Joachim
Virtual Vehicle Research GmbH, 8010 Graz, Austria.
Institute of Automation and Control, Graz University of Technology, 8010 Graz, Austria.
Sensors (Basel). 2022 Sep 8;22(18):6798. doi: 10.3390/s22186798.
At present, autonomous driving vehicles are designed in an ego-vehicle manner. The vehicles gather information from their on-board sensors, build an environment model from it and plan their movement based on this model. Mobile network connections are used for non-mission-critical tasks and maintenance only. In this paper, we propose a connected autonomous driving system, where self-driving vehicles exchange data with a so-called road supervisor. All vehicles under supervision provide their current position, velocity and other valuable data. Using the received information, the supervisor provides a recommended trajectory for every vehicle, coordinated with all other vehicles. Since the supervisor has a much better overview of the situation on the road, more elaborate decisions, compared to each individual autonomous vehicle planning for itself, are possible. Experiments show that our approach works efficiently and safely when running our road supervisor on top of a popular traffic simulator. Furthermore, we show the feasibility of offloading the trajectory planning task into the network when using ultra-low-latency 5G networks.
目前，自动驾驶车辆是以自我车辆的方式设计的。车辆从其车载传感器收集信息，据此构建环境模型，并基于该模型规划其行驶路线。移动网络连接仅用于非关键任务和维护。在本文中，我们提出了一种联网自动驾驶系统，其中自动驾驶车辆与所谓的道路监管器交换数据。所有受监管车辆提供其当前位置、速度和其他有价值的数据。监管器利用接收到的信息，为每辆车提供与所有其他车辆协调的推荐轨迹。由于监管器对道路情况有更好的总体了解，与每辆单独的自动驾驶车辆自行规划相比，能够做出更精细的决策。实验表明，当在一个流行的交通模拟器之上运行我们的道路监管器时，我们的方法能够高效且安全地运行。此外，我们展示了在使用超低延迟5G网络时将轨迹规划任务卸载到网络中的可行性。
