Safe-driving cloning by deep learning for autonomous cars (original) (raw)
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Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks
Recent Patents on Computer Science 2019, 12, 1-8, 2019
In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. This data is then used to train the proposed CNN to facilitate what it is called "Behavioral Cloning". The proposed Behavior Cloning CNN is named as "BCNet", and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam's optimization algorithm as a variant of the Stochas-tic Gradient Descent (SGD) technique. The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.
Behavior Cloning for Autonomous Driving using Convolutional Neural Networks
2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2018
In this paper, we propose using a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering maneuvering as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. This data is then used to train the proposed CNN to facilitate what we call it behavioral cloning. The proposed Behavior Cloning CNN is named as “BCNet” and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Scholastic Gradient Descent (SGD) technique. The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.
Simulation of Autonomous Car using Deep Learning
International Journal of Innovative Research in Technology, 2021
In this world of rapid advancement of computer technologies, like CNN, open-cv etc., Deep learning has grown tremendously in the field of artificial intelligence and can be used to automate almost anything, that includes modern technologies. These technologies can be applied to the car so that it requires minimum interaction with driver to run on the road or we can say that to program a car in such way that it drives in self-governing mode. With help of existing simulator, we can use it to generate the enormous amount of data that includes images and csv containing car details. We train the network with generated images (left, right, center) to predict the required steer angle to keep the car on track. This approach decreases down the resolution of images to train the network very rapidly. Before sending the data to network it is preprocessed which is very much beneficial. After the preprocessing data is send to convolutional neural network in form of fixed size batches formed by random collection of images with their corresponding steering angle within the dataset generated to train and predict the steering angle as a final result. The Model achieved better performance when it is provided even more dataset. Here, we observe many Convolutional neural network architectures to obtain better performance with lesser load.
Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To drive vehicles autonomously, controlling steer angle, gas hatch, and brakes need to be learned. The behavioral cloning method is used to imitate humans’ driving behavior. We created a dataset of driving in different routes and conditions and using the designed model, the output used for controlling the vehicle is obtained. In this paper, the Learning of Self-driving Vehicles Based on Real Driving Behavior Using Deep Neural Network Techniques (LSV-DNN) is proposed. We designed a convolutional network which uses the real driving data obtained through the vehicle’s camera and computer. The response of the driver is during driving is recorded in different situations and by converting the real driver’s driving video to images and transferring the data to an excel file, obstacle detection is carried out with the best accuracy and speed using the Yolo algorith...
From Pixels to Actions: Learning to Drive a Car with Deep Neural Networks
2018
The promise of self-driving cars promotes several advantages, e.g. they have the ability to outperform human drivers while being safer. Here we take a deeper look into some aspects from algorithms aimed at making this promise a reality. More specifically, we analyze an end-to-end neural network to predict a car's steering actions on a highway based on images taken from a single car-mounted camera. We focus our analysis on several aspects which could have a significant impact on the performance of the system. These aspects are: the input data format, the temporal dependencies between consecutive inputs, and the origin of the data. We show that, for the task at hand, regression networks outperform their classifier counterparts. In addition, there seems to be a small difference between networks that use coloured images and ones that use grayscale images as input. For the second aspect, by feeding the network three concatenated images, we get a significant decrease of 30% in mean squared error. For the third aspect, by using simulation data we are able to train networks that have a performance comparable to networks trained on real-life datasets. We also qualitatively demonstrate that the standard metrics that are used to evaluate networks do not necessarily accurately reflect a system's driving behaviour. We show that a promising confusion matrix may result in poor driving behaviour while a very ill-looking confusion matrix may result in good driving behaviour.
Self Driving Car using Deep Learning Technique
International Journal of Engineering Research and, 2020
The biggest challenge of a self-driving car is autonomous lateral motion so the main aim of this paper is to clone drives for better performance of the autonomous car for which we are using multilayer neural networks and deep learning techniques. We will focus to achieve autonomous cars driving in stimulator conditions. Within the simulator, preprocessing the image obtained from the camera placed in the car imitate the driver's vision and then the reaction, which is the steering angle of the car. The neural network trains the deep learning technique on the basis of photos taken from a camera in manual mode which provides a condition for running the car in autonomous mode, utilizing the trained multilayered neural network. The driver imitation algorithm fabricated and characterized in the paper is all about the profound learning technique that is centered around the NVIDIA CNN model.
An Experimental Analysis on Self Driving Car Using CNN
IRJET, 2022
For the past decade, there has been a surge of interest in self-driving cars. This can be because of breakthroughs within the field of deep learning wherever deep neural networks square measure trained to perform tasks that generally need human intervention. CNN’s apply models to spot patterns and options in pictures, creating them helpful within the field of pc Vision. Samples of these square measure object detection, image classification, image captioning, etc. during this project, we've trained a CNN victimization pictures captured by a simulated automotive to drive the automotive autonomously. The CNN learns distinctive options from the pictures and generates steering predictions permitting the automotive to drive while not somebody's. For testing functions and getting ready the dataset the Unity based mostly machine provided by Udacity was used
Application of Deep Learning to Autonomous Robotic Car
International Journal of Computer Applications , 2021
Autonomous machines are becoming prevalent, even more so the advent of autonomous vehicles. While autonomous cars have been around for some time, the endless innovations in this domain have led to the removal of human-in-the loop, hence constantly seeking to remove human input while delivering optimal result. However, safety is a major concern, and users are wary of leaving safety level decisions to machines. There is a rise in road accident caused by autonomous cars, while some have blamed it on human's total trust in machines, and researchers have called for the development of human-level accurate algorithms to tackle decision making using state-of-the-art techniques. Therefore, this paper seeks to use computer vision leveraging on deep learning techniques to detect pedestrians, traffic signs, important objects, and lane lines to infer crucial driver decisions.Mask R-Convolutional Neural Network (CNN) was used for object classification with the aid of transfer learning saving the hassle of training and GPU times. A simple method for collecting data was applied using a wide-anglecamera and using Google TPU to perform real time object recognition without the need for a GPU enabled machine.
Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
IEEE Transactions on Intelligent Transportation Systems, 2021
Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-theart strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.
Modelling and Simulation in Engineering
End-to-end learning for autonomous driving uses a convolutional neural network (CNN) to predict the steering angle from a raw image input. Most of the solutions available for end-to-end autonomous driving are computationally too expensive, which increases the inference of autonomous driving in real time. Therefore, in this paper, CNN architecture has been trained which is lightweight and achieves comparable results to Nvidia’s PilotNet. The data used to train and evaluate the network is collected from the Car Learning to Act (CARLA) simulator. To evaluate the proposed architecture, the MSE (mean squared error) is used as the performance metric. Results of the experiment shows that the proposed model is 4x lighter than Nvidia’s PilotNet in term of parameters but still attains comparable results to PilotNet. The proposed model has achieved 5.1 × 10 − 4 MSE on testing data while PilotNet MSE was 4.7 × 10 − 4 .