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Showing 6 results for Subject: Autonomous vehicles

T. Feyzi, M. Esfahanian, R. Tikani, S. Ziaei Rad,
Volume 1, Issue 2 (6-2011)

Mrs Mina Zohoorian Yazdi, Dr. Mohsen Soryani,
Volume 9, Issue 3 (9-2019)

Today most accidents are caused by drivers’ fatigue, drowsiness and losing attention on the road ahead. In this paper, a system is introduced, using RGB-D cameras to automatically identify drowsiness and give warning. In this system two important modules have been utilized simultaneously to identify the state of driver’s mouth and eyes for detecting drowsiness. At first, using the depth information, the mouth area and its state are identified. Then using CNN networks, to predict whether the eyes are open or closed, a semi-VGG architecture is used .The results of yawning and eyes states detection are integrated to decide whether an alarm should be issued. The results show an accuracy of about 90% which is encouraging.
Alireza Khodayari, Arya Yahyaei,
Volume 10, Issue 2 (6-2020)

In this paper, an intelligent system based on a novel algorithm for pulling out is designed and implemented. Through processing images of the surroundings of a vehicle, this very algorithm detects the obstacles and objects which are likely to pose danger to the vehicle while pulling out.  Two different methods are integrated into this system to detect obstacles and objects.  The first method, which is called Support Vector Machine (SVM), detects a broad range of moving objects around the vehicle drawing on training datasets.  Whereas, in the second method, types of obstacles and objects are detected using the area of the moving object within range. The system in question is implemented using both methods whose performance are compared in terms of computation and image processing speed; SVM and object area methods detected 93% and 96% of vehicles respectively. The utilization of this algorithm can contribute to the safety of vehicles while executing pullout maneuver and decreased the probability of crashing into fixed and moving obstacles in the surroundings.  Results of this research will be available to be used in the design and development of parking control systems. 
Dr. Alireza Bosaghzadeh, Majid Nasiri Manjili,
Volume 10, Issue 3 (9-2020)

Lane detection is a crucial step for any autonomous driving system to decrease car accidents and increase safety. In this paper, based on inverse perspective mapping and Probabilistic Hough Transform, we propose a lane detection system which works on city street images. First, by using inverse perspective mapping the top view of the street is obtained. Second, the lanes are rectified using a specifically designed filter which enhances the lanes and suppresses other elements. Then, by using Probabilistic Hough transform the location of the lanes is detected in the images. For the final refinement, lane candidates are mapped to the road image using perspective mapping and the lane intensity is analyzed to reduce false acceptance. We evaluate the performance of the proposed method on Caltech-lane dataset and the obtained results show that the proposed method is able to detect straight lanes.
Behzad Samani, Dr Amir Hossein Shamekhi,
Volume 11, Issue 1 (3-2021)

In this paper, an adaptive cruise control system is designed that is controlled by a neural network model. This neural network model is trained with data resulting from the simulation of a multi-objective nonlinear predictive adaptive cruise control system. For this purpose, first, an adaptive cruise control system was designed using the concept of model predictive control based on a nonlinear model to maintain the desired speed of the driver, maintain a safe distance with the car in front, reducing fuel consumption and increasing ride comfort. Due to the time-consuming computations in predictive control systems and the consequent need for powerful and expensive hardware, it was decided to use the extracted data from the simulation of this designed cruise control system to train a neural network model and use this model to achieve control objectives instead of the predictive controller. Using the neural network model in the cruise control system, despite a significant reduction in computation time, the control objectives were well achieved, and in fact a combination of model predictive controller accuracy and neural network controller speed was used.

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