Vision and Dead Reckoning-based End-to-End Parking for Autonomous Vehicles

In this paper, a combined vision and dead reckoning-based parking system for end-to-end driving are proposed. Standard autonomous parking frameworks contain multiple modules with each module having its own limitation. On the other hand, the proposed parking framework consists of a single end-to-end module, which reduces these inherent limitations. In the proposed deep learning-based parking system, a novel iterative two-stage learning framework is utilized to predict the steering angles and gear status using a front and back mounted monocular camera. In the first stage of the proposed framework, the encoder-decoder architecture is used to predict an initial estimate of the steering angle trajectory from multiple frames of the front or the back monocular camera. The camera used for steering estimated is selected using the gear status estimate. The gear status is predefined during initialization and estimated subsequently in the second stage of the proposed framework. In the second stage of the proposed framework, the initial estimate of the steering angle trajectory along with the vehicles heading angle, and absolute position is given as an input to the long short-term memory network to estimate the optimal steering angle and gear status. The proposed framework is validated on an acquired dataset. A comparative analysis of baseline algorithms and detailed parametric analysis are performed. The experimental results show that the proposed framework is better than the baseline end-to-end algorithms.