scholarly journals An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning

2018 ◽  
Vol 8 (7) ◽  
pp. 1164 ◽  
Author(s):  
YiNa Jeong ◽  
SuRak Son ◽  
EunHee Jeong ◽  
ByungKwan Lee
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Edge Computing ◽  
The Self ◽  
Training Dataset ◽  
Area Network ◽  
Diagnosis System ◽  
Vehicle Sensors ◽  
Media Data

This paper proposes “An Integrated Self-diagnosis System (ISS) for an Autonomous Vehicle based on an Internet of Things (IoT) Gateway and Deep Learning” that collects information from the sensors of an autonomous vehicle, diagnoses itself, and the influence between its parts by using Deep Learning and informs the driver of the result. The ISS consists of three modules. The first In-Vehicle Gateway Module (In-VGM) collects the data from the in-vehicle sensors, consisting of media data like a black box, driving radar, and the control messages of the vehicle, and transfers each of the data collected through each Controller Area Network (CAN), FlexRay, and Media Oriented Systems Transport (MOST) protocols to the on-board diagnostics (OBD) or the actuators. The data collected from the in-vehicle sensors is transferred to the CAN or FlexRay protocol and the media data collected while driving is transferred to the MOST protocol. Various types of messages transferred are transformed into a destination protocol message type. The second Optimized Deep Learning Module (ODLM) creates the Training Dataset on the basis of the data collected from the in-vehicle sensors and reasons the risk of the vehicle parts and consumables and the risk of the other parts influenced by a defective part. It diagnoses the vehicle’s total condition risk. The third Data Processing Module (DPM) is based on Edge Computing and has an Edge Computing based Self-diagnosis Service (ECSS) to improve the self-diagnosis speed and reduce the system overhead, while a V2X based Accident Notification Service (VANS) informs the adjacent vehicles and infrastructures of the self-diagnosis result analyzed by the OBD. This paper improves upon the simultaneous message transmission efficiency through the In-VGM by 15.25% and diminishes the learning error rate of a Neural Network algorithm through the ODLM by about 5.5%. Therefore, in addition, by transferring the self-diagnosis information and by managing the time to replace the car parts of an autonomous driving vehicle safely, this reduces loss of life and overall cost.

scholarly journals A Design of a Lightweight In-Vehicle Edge Gateway for the Self-Diagnosis of an Autonomous Vehicle

2018 ◽  
Vol 8 (9) ◽  
pp. 1594 ◽  
Author(s):  
YiNa Jeong ◽  
SuRak Son ◽  
EunHee Jeong ◽  
ByungKwan Lee
Keyword(s):  
Delay Time ◽  
Autonomous Vehicle ◽  
Cost Effective ◽  
Transmission Error ◽  
Control Area ◽  
Transmission Delay ◽  
The Self ◽  
Area Network ◽  
Address Mapping ◽  
Control Area Network

This paper proposes a Lightweight In-Vehicle Edge Gateway (LI-VEG) for the self-diagnosis of an autonomous vehicle, which supports a rapid and accurate communication between in-vehicle sensors and a self-diagnosis module and between in-vehicle protocols. A paper on the self-diagnosis module has been published previously, thus this paper only covers the LI-VEG, not the self-diagnosis. The LI-VEG consists of an In-Vehicle Sending and Receiving Layer (InV-SRL), an InV-Management Layer (InV-ML) and an InV-Data Translator Layer (InV-DTL). First, the InV-SRL receives the messages from FlexRay, Control Area Network (CAN), Media Oriented Systems Transport (MOST), and Ethernet and transfers the received messages to the InV-ML. Second, the InV-ML manages the message transmission and reception of FlexRay, CAN, MOST, and Ethernet and an Address Mapping Table. Third, the InV-DTL decomposes the message of FlexRay, CAN, MOST, and Ethernet and recomposes the decomposed messages to the frame suitable for a destination protocol. The performance analysis of the LI-VEG shows that the transmission delay time about message translation and transmission is reduced by an average of 10.83% and the transmission delay time caused by traffic overhead is improved by an average of 0.95%. Therefore, the LI-VEG has higher compatibility and is more cost effective because it applies a software gateway to the OBD, compared to a hardware gateway. In addition, it can reduce the transmission error and overhead caused by message decomposition because of a lightweight message header.

scholarly journals Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 360
Author(s):  
Theyazn H. H. Aldhyani ◽  
Hasan Alkahtani
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Can Bus ◽  
Autonomous Vehicle ◽  
Hybrid Network ◽  
Superior Performance ◽  
Area Network ◽  
Vehicle Network

Rapid technological development has changed drastically the automotive industry. Network communication has improved, helping the vehicles transition from completely machine- to software-controlled technologies. The autonomous vehicle network is controlled by the controller area network (CAN) bus protocol. Nevertheless, the autonomous vehicle network still has issues and weaknesses concerning cybersecurity due to the complexity of data and traffic behaviors that benefit the unauthorized intrusion to a CAN bus and several types of attacks. Therefore, developing systems to rapidly detect message attacks in CAN is one of the biggest challenges. This study presents a high-performance system with an artificial intelligence approach that protects the vehicle network from cyber threats. The system secures the autonomous vehicle from intrusions by using deep learning approaches. The proposed security system was verified by using a real automatic vehicle network dataset, including spoofing, flood, replaying attacks, and benign packets. Preprocessing was applied to convert the categorical data into numerical. This dataset was processed by using the convolution neural network (CNN) and a hybrid network combining CNN and long short-term memory (CNN-LSTM) models to identify attack messages. The results revealed that the model achieved high performance, as evaluated by the metrics of precision, recall, F1 score, and accuracy. The proposed system achieved high accuracy (97.30%). Along with the empirical demonstration, the proposed system enhanced the detection and classification accuracy compared with the existing systems and was proven to have superior performance for real-time CAN bus security.

scholarly journals Deep Learning Implementation of Autonomous Driving using Ensemble-M in Simulated Environment

2021 ◽  
Author(s):  
Meenu Gupta ◽  
Vikalp Upadhyay ◽  
Prince Kumar ◽  
Fadi Al-Turjman
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Computation Time ◽  
Autonomous Driving ◽  
Human Mind ◽  
Simulated Environment ◽  
Secondary Objective ◽  
Impossible Task ◽  
Time Utilization ◽  
Result Analysis

Abstract Making Autonomous Driving a safe, feasible and better alternative is one the core problems the world is facing today. The horizon of the applications of AI and Deep Learning has changed the perspective of the human mind. Initially, what used to be thought as subtle impossible task is applicable today and that too in the feasibly efficient way. Computer vision tasks powered with highly tuned CNNs are outperforming humans in many fields. Introductory implementations of autonomous vehicle were merely achieved using raw image processing and hard programmed rule-based logic systems along with machine/deep Learning used as secondary objective handlers. With the autonomous driving method proposed by NVIDIA, the usability of CNNs is more adequate, adaptable and applicable. In this paper, we propose, the ensemble implementation of CNN based regression models for autonomous-driving. We have taken simulator generated driving view image dataset along with mapped file of steering angle in radians. After applying image pre-processing and augmentation, we have used two CNN models along with their ensemble and compared their performance as to minimize the risks of unsafe driving. We have compared Nvidia proposed CNN, MobileNet-V2 as regression model and Ensemble-M results for comparison their respective performance, MSE scores and compute time to process. In result analysis, the MobileNet-V2 model performs better in densely-featured roads and Nvidia model performs better in sparsely-featured roads whereas Ensemble-M normalizes the performance of both models and efficiently result in the least MSE score (0.0201) with highest computation time utilization.

scholarly journals Behaviour Cloning for Autonomous Driving

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 694-705
Author(s):  
T. Kirthiga Devi ◽  
Akshat Srivatsava ◽  
Kritesh Kumar Mudgal ◽  
Ranjnish Raj Jayanti ◽  
T. Karthick
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Lane Detection ◽  
Human Intervention ◽  
Steering Angle ◽  
Steering Mechanism ◽  
Deep Learning Model

The objective of this project is to automate the process of driving a car. The result of this project will surely reduce the number of hazards happening everyday. Our world is in progress and self driving car is on its way to reach consumer‟s door-step but the big question still lies that will people accept such a car which is fully automated and driverless. The idea is to create an autonomous Vehicle that uses only some sensors (collision detectors, temperature detectors etc.) and camera module to travel between destinations with minimal/no human intervention. The car will be using a trained Convolutional Neural Network (CNN) which would control the parameters that are required for smoothly driving a car. They are directly connected to the main steering mechanism and the output of the deep learning model will control the steering angle of the vehicle. Many algorithms like Lane Detection, Object Detection are used in tandem to provide the necessary functionalities in the car.

scholarly journals Curve Path Detection in Autonomous Vehicle using Deep Learning

2018 ◽  
Author(s):  
Balasriram Kodi ◽  
Manimozhi M
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Ground Truth ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Point To Point ◽  
And Control ◽  
Mapping Point

In the field of autonomous vehicles, lane detection and control plays an important role. In autonomous driving the vehicle has to follow the path to avoid the collision. A deep learning technique is used to detect the curved path in autonomous vehicles. In this paper a customized lane detection algorithm was implemented to detect the curvature of the lane. A ground truth labelling tool box for deep learning is used to detect the curved path in autonomous vehicle. By mapping point to point in each frame 80-90% computing efficiency and accuracy is achieved in detecting path.

scholarly journals An Overview of the Latest Progress and Core Challenge of Autonomous Vehicle Technologies

2020 ◽  
Vol 308 ◽  
pp. 06002
Author(s):  
Zongwei Liu ◽  
Hao Jiang ◽  
Hong Tan ◽  
Fuquan Zhao
Keyword(s):  
Deep Learning ◽  
Mass Production ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Future Trend ◽  
Rapid Progress ◽  
Vehicle Technologies

The mass production of autonomous vehicle is coming, thanks to the rapid progress of autonomous driving technology, especially the recent breakthroughs in LiDAR sensors, GPUs, and deep learning. Many automotive and IT companies represented by Waymo and GM are constantly promoting their advanced autonomous vehicles to hit public roads as early as possible. This paper systematically reviews the latest development and future trend of the autonomous vehicle technologies, discusses the extensive application of AI in ICV, and identifies the key problems and core challenges facing the commercialization of autonomous vehicle. Based on the review, it forecasts the prospects and conditions of autonomous vehicle’s mass production and points out the arduous, long-term and systematic nature of its development.

scholarly journals Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Training Dataset ◽  
Growth Stages ◽  
3D Point Cloud ◽  
Plant Structure ◽  
Data Annotation ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Different Growth Stages

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

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