  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.
