Multi-Objective Evolutionary Algorithm -assisted automated parallel parking

The ease with which a human expert driver performs the complex tasks involved in parallel-parking a non-holonomic vehicle motivates the mimicry of an human driving behavior in automation of the task. This paper presents such an algorithm to achieve automated parallel parking in tight spaces. Unlike other approaches rooted in neural networks and/or fuzzy logic, the proposed algorithm performs maneuvers closely modeled after human driving instructions. Stevenspsila power law is employed in modeling perceived physical quantities on which the instructions operate while the uncertainty inherent in the natural language formulation is represented by Gaussian distribution. The algorithm consists of five stages: position alignment in preparation for the backward S-turn, the first half of the S-turn, position alignment for the second part of the S-turn, the second part of the S-turn and longitudinal adjustment. Negotiation of available parking space in the second part of the S-turn, arguably the most difficult part, is performed with the help of a rule base documenting the relation between steering angle, vehicle orientation and distance traversed. To achieve parking accuracy and avoid collision in the maneuver, the appropriate steering angle must be employed. This angle is approximated from the most suitable rule, which identification is essentially a multi-objective problem addressed here by a Multi-Objective Evolutionary Algorithm. Computer simulations demonstrate the success of the approach.