  Automated Parking is becoming a standard feature in modern vehicles. Existing
parking systems build a local map to be able to plan for maneuvering towards a
detected slot. Next generation parking systems have an use case where they
build a persistent map of the environment where the car is frequently parked,
say for example, home parking or office parking. The pre-built map helps in
re-localizing the vehicle better when its trying to park the next time. This is
achieved by augmenting the parking system with a Visual SLAM pipeline and the
feature is called trained trajectory parking in the automotive industry. In
this paper, we discuss the use cases, design and implementation of a trained
trajectory automated parking system. The proposed system is deployed on
commercial vehicles and the consumer application is illustrated in
\url{https://youtu.be/nRWF5KhyJZU}. The focus of this paper is on the
application and the details of vision algorithms are kept at high level.
