دانلود رایگان مقاله انگلیسی یک مدل نوین و مستقل برای تاکسی در شهرهای هوشمند - IEEE 2018

Abstract

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.

CONCLUSIONS

In smart city scenarios, autonomous taxies will be playing significant role for implementing intelligent public transportation systems. In this direction, low cost and environment friendly solutions are electrically powered autonomous taxies with single front camera based computationally less complex systems that are inspired by human driving capability. Humans take visual perceptions and draw inferences for better driving by using their past experiences and estimate the optimal driving path. In this work, we have demonstrated a novel and simpler obstacle avoidance and path planning system for implementing autonomous taxies using deep neural networks and by mimicking human decision making approach for efficient and autonomous driving. Such intelligent systems are more efficient, robust and promising.