In recent years, significant advancements have been made in the field of autonomous driving with the aim of increasing safety and efficiency. However, research that focuses on tractor-trailer vehicles is relatively sparse. Due to the physical characteristics and articulated joints, such vehicles require tailored models. While turning, the back wheels of the trailer turn at a tighter radius and the truck often has to deviate from the centre of the lane to accommodate this. Due to the lack of publicly available models, this work develops truck and trailer models using the high-fidelity simulation software CARLA, together with several roundabout scenarios, to establish a baseline dataset for benchmarks. Using a twin-q soft actor-critic algorithm, we train a quasi-end-to-end autonomous driving model which is able to achieve a 73% success rate on different roundabouts.

最近几年，在自动驾驶领域取得了重大进展，旨在提高安全性和效率。然而，专注于半挂车辆的研究相对较少。由于物理特性和铰接连接，这些车辆需要定制的模型。通过使用高保真模拟软件CARLA和几个环形交叉口场景，本研究开发了卡车和挂车模型，并建立了基准数据集。使用双q软件参与者算法，我们训练了一个准端到端的自动驾驶模型，在不同的环形交叉口上能够达到73%的成功率。

自动导航拖车车辆通过环岛交叉口