Challenges in visual parking and how a developmental network approaches the problem

Many existing parking assistance systems use LIDAR and range sensors. However, such sensors may be fooled by dark surfaces, wet surfaces, and mirrors. The range information does not tell restricted parking spaces such as handicap parking spaces. Vision based autonomous navigation methods have a potential to use richer and more redundant intensity and color information but also require sophisticated processing and extensive computation. In this paper we present our vision based autonomous parking agent for large parking ramps using an object recognition neural network. After action-supervised learning, the network is able to find empty spaces in indoor parking ramps, park into space, and leave the parking space with no human guidance/supervision. An Finite Automaton as context-dependent rules emerge from the Developmental Network. The transitions between different parking states are facilitated by the recognized landmarks learned through on-line training process. Our experiments showed that the agent learned to maneuver in novel environments at a high action accuracy. Our future work includes training and testing the agent in real-time.