A Novel Autonomous Taxi Model for Smart Cities

P. 625–628.

Rajput N. S., Deogune M., Mishra A., Kumar A., Makarov I.

Autonomous taxies are in high demand for smart city scenario. Such taxies have a well specified path to travel. Therefore, these vehicles only required two important parameters. One is detection parameter and other is control parameter. Further, detection parameters require turn detection and obstacle detection. The control parameters contain steering control and speed control. In this paper a novel autonomous taxi model has been proposed for smart city scenario. Deep learning has been used to model the human driver capabilities for the autonomous taxi. A hierarchical Deep Neural Network (DNN) architecture has been utilized to train various driving aspects. In first level, the proposed DNN architecture classifies the straight and turning of road. A parallel DNN is used to detect obstacle at level one. In second level, the DNN discriminates the turning i.e. left or right for steering and speed controls. Two multi layered DNNs have been used on Nvidia Tesla K 40 GPU based system with Core i-7 processor. The mean squared error (MSE) for the detection parameters viz. speed and steering angle were 0.018 and 0.0248 percent, respectively, with 15 milli seconds of realtime response delay.

Keywords: deep learning, глубокое обучение, Autonomous Taxi, Driver assistance systems, Lane detection, Smart City formatting, Автономные автомобили

Publication based on the results of: Applied network research with big data and new technological advances (2018)

In book

Proceedings of 4th IEEE World Forum on Internet of Things WF-IoT 2018

NY: IEEE Computer Society, 2018.