  Automated driving is an active area of research in both industry and
academia. Automated Parking, which is automated driving in a restricted
scenario of parking with low speed manoeuvring, is a key enabling product for
fully autonomous driving systems. It is also an important milestone from the
perspective of a higher end system built from the previous generation driver
assistance systems comprising of collision warning, pedestrian detection, etc.
In this paper, we discuss the design and implementation of an automated parking
system from the perspective of computer vision algorithms. Designing a low-cost
system with functional safety is challenging and leads to a large gap between
the prototype and the end product, in order to handle all the corner cases. We
demonstrate how camera systems are crucial for addressing a range of automated
parking use cases and also, to add robustness to systems based on active
distance measuring sensors, such as ultrasonics and radar. The key vision
modules which realize the parking use cases are 3D reconstruction, parking slot
marking recognition, freespace and vehicle/pedestrian detection. We detail the
important parking use cases and demonstrate how to combine the vision modules
to form a robust parking system. To the best of the authors' knowledge, this is
the first detailed discussion of a systemic view of a commercial automated
parking system.
