Autonomous driving technology enables new and innovative driverless vehicle concepts to emerge, like U-Shift. Designed from the ground up, the U-Shift II platform, called driveboard, exemplifies the advantages of separating a vehicle's driving capability from the intended transportation task. It allows different so-called capsules, such as public transport or cargo, to be transported using the same U-shaped driving platform. The driveboard can change the capsules autonomously, thus providing high flexibility for fleet operators. This novel approach introduces new challenges to the task of autonomous driving. On one hand, changing sensor and vehicle configurations, e.g., when transporting a capsule with its own sensors to compensate for occlusions of the driveboard sensors by the capsule itself, requires an adaptive approach to environmental perception. On the other hand, different environments and driving tasks, as well as the augmented motion capabilities of the driveboard, require novel motion planning and control algorithms that also adapt to changing vehicle configurations. For example, the driveboard's automatic pick-up of capsules places high demands on perception and planning precision. In this paper, we first present our automation concept for the U-Shift II vehicle, which comprises environment perception and motion planning, emphasizing the modifications and enhancements compared to state-of-the-art autonomous vehicles. Second, we present a proof of concept focusing on flexibility and adaptivity based on a software-in-the-loop simulation using CARLA.