Deep learning based Blockchain solution for preserving privacy in future vehicles

2020 ◽  
pp. 1-14
Author(s):  
Varsha R ◽  
Meghna Manoj Nair ◽  
Siddharth M. Nair ◽  
Amit Kumar Tyagi
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Recommendation System ◽  
Privacy Preserving ◽  
Smart Devices ◽  
The Road ◽  
Blockchain Technology ◽  
Technological Advances ◽  
Previous Decade ◽  
Smart Things

The Internet of Things (smart things) is used in many sectors and applications due to recent technological advances. One of such application is in the transportation system, which is of primary use for the users to move from one place to another place. The smart devices which were embedded in vehicles are useful for the passengers to solve his/her query, wherein future vehicles will be fully automated to the advanced stage, i.e. future cars with driverless feature. These autonomous cars will help people a lot to reduce their time and increases their productivity in their respective (associated) business. In today’s generation and in the near future, privacy preserving and trust will be a major concern among users and autonomous vehicles and hence, this paper will be able to provide clarity for the same. Many attempts in previous decade have provided many efficient mechanisms, but they all work only with vehicles along with a driver. However, these mechanisms are not valid and useful for future vehicles. In this paper, we will use deep learning techniques for building trust using recommender systems and Blockchain technology for privacy preserving. We also maintain a certain level of trust via maintaining the highest level of privacy among users living in a particular environment. In this research, we developed a framework that could offer maximum trust or reliable communication to users over the road network. With this, we also preserve privacy of users during traveling, i.e., without revealing identity of respective users from Trusted Third Parties or even Location Based Service in reaching a destination. Thus, Deep Learning based Blockchain Solution (DLBS) is illustrated for providing an efficient recommendation system.

scholarly journals Road-Aware Trajectory Prediction for Autonomous Driving on Highways

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4703
Author(s):  
Yookhyun Yoon ◽  
Taeyeon Kim ◽  
Ho Lee ◽  
Jahnghyon Park
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Prediction Method ◽  
Autonomous Driving ◽  
Structural Constraints ◽  
High Definition ◽  
Trajectory Prediction ◽  
The Road ◽  
Road Geometry ◽  
Efficient Learning

For driving safely and comfortably, the long-term trajectory prediction of surrounding vehicles is essential for autonomous vehicles. For handling the uncertain nature of trajectory prediction, deep-learning-based approaches have been proposed previously. An on-road vehicle must obey road geometry, i.e., it should run within the constraint of the road shape. Herein, we present a novel road-aware trajectory prediction method which leverages the use of high-definition maps with a deep learning network. We developed a data-efficient learning framework for the trajectory prediction network in the curvilinear coordinate system of the road and a lane assignment for the surrounding vehicles. Then, we proposed a novel output-constrained sequence-to-sequence trajectory prediction network to incorporate the structural constraints of the road. Our method uses these structural constraints as prior knowledge for the prediction network. It is not only used as an input to the trajectory prediction network, but is also included in the constrained loss function of the maneuver recognition network. Accordingly, the proposed method can predict a feasible and realistic intention of the driver and trajectory. Our method has been evaluated using a real traffic dataset, and the results thus obtained show that it is data-efficient and can predict reasonable trajectories at merging sections.

scholarly journals Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles

2021 ◽  
Vol 11 (17) ◽  
pp. 7984
Author(s):  
Prabu Subramani ◽  
Khalid Nazim Abdul Sattar ◽  
Rocío Pérez de Prado ◽  
Balasubramanian Girirajan ◽  
Marcin Wozniak
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Feature Fusion ◽  
Domain Adaptation ◽  
Road Detection ◽  
Convolutional Network ◽  
The Road ◽  
Learning Classifiers ◽  
On The Road ◽  
Institute Of Technology

Connected autonomous vehicles (CAVs) currently promise cooperation between vehicles, providing abundant and real-time information through wireless communication technologies. In this paper, a two-level fusion of classifiers (TLFC) approach is proposed by using deep learning classifiers to perform accurate road detection (RD). The proposed TLFC-RD approach improves the classification by considering four key strategies such as cross fold operation at input and pre-processing using superpixel generation, adequate features, multi-classifier feature fusion and a deep learning classifier. Specifically, the road is classified as drivable and non-drivable areas by designing the TLFC using the deep learning classifiers, and the detected information using the TLFC-RD is exchanged between the autonomous vehicles for the ease of driving on the road. The TLFC-RD is analyzed in terms of its accuracy, sensitivity or recall, specificity, precision, F1-measure and max F measure. The TLFC- RD method is also evaluated compared to three existing methods: U-Net with the Domain Adaptation Model (DAM), Two-Scale Fully Convolutional Network (TFCN) and a cooperative machine learning approach (i.e., TAAUWN). Experimental results show that the accuracy of the TLFC-RD method for the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset is 99.12% higher than its competitors.

Blockchain Security Using Secure Multi-Party Computation

2021 ◽  
pp. 178-195
Author(s):  
Jenila Livingston L. M. ◽  
Ashutosh Satapathy ◽  
Agnel Livingston L. G. X. ◽  
Merlin Livingston L. M.
Keyword(s):  
Autonomous Vehicles ◽  
Cooperative Control ◽  
Personal Information ◽  
Autonomous Vehicle ◽  
Privacy Preserving ◽  
Human Interaction ◽  
Security And Privacy ◽  
Blockchain Technology ◽  
Vehicle Data ◽  
Real Model

In secure multi-party computation (SMC), multiple distributed parties jointly carry out the computation over their confidential data without compromising data security and privacy. It is a new emerging cryptographic technique used in huge applications such as electronic auction bidding, electronic voting, protecting personal information, secure transaction processing, privacy preserving data mining, and privacy preserving cooperative control of connected autonomous vehicles. This chapter presents two model paradigms of SMC (i.e., ideal model prototype and real model prototype). It also deals with the type and applications of adversaries, properties, and the techniques of SMC. The three prime types of SMC techniques such as randomization, cryptographic techniques using oblivious transfer, and anonymization methods are discussed and illustrated by protective procedures with suitable examples. Finally, autonomous vehicle interaction leveraged with blockchain technology to store and use vehicle data without any human interaction is also discussed.

Blockchain-Enabled Decentralization Service for Automated Parking Systems

2021 ◽  
pp. 51-63
Author(s):  
Keesara Sravanthi Reddy
Keyword(s):  
Personal Information ◽  
Denial Of Service ◽  
Third Party ◽  
Smart Devices ◽  
Primary Concern ◽  
Long Distance ◽  
The Road ◽  
Blockchain Technology ◽  
Information User ◽  
Automatic Parking

Due to recent development in technology and smart devices in people's lives, their lives are becoming easier and safer. One of popular examples in todays is parking (i.e., people find free parking space without moving a long distance or consuming more time or fuel over the road network). Today many automated companies are designing vehicles, but we are still unable to get automatic parking system in an area. Finding free parking slot/space has a probability of revealing user's privacy (i.e., either by service provider to third party/attacker or submitted information [user personal information] can be hacked by an attacker [via performing attacks like Man in Middle, Denial of Service, etc.]). Hence, privacy is a main issue in parking. Providing sufficient privacy in parking to vehicle users is a primary concern of this chapter. For that, this chapter used the blockchain technology to avoid privacy issues (raised in parking searching). Blockchain technology makes reservation of parking slot transparent, decentralized, and secure (privacy-preserved).

Regaining Sight of Humanity on The Roadway towards Automation

2020 ◽  
Vol 2020 (16) ◽  
pp. 88-1-88-5
Author(s):  
Mónica López-González
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Machine Learning Algorithms ◽  
Technological Advancement ◽  
Cognitive Intelligence ◽  
Safety Standards ◽  
The Road ◽  
Technological Advances ◽  
On The Road ◽  
Problem Solve

A primary goal of the auto industry is to revolutionize transportation with autonomous vehicles. Given the mammoth nature of such a target, success depends on a clearly defined balance between technological advances, machine learning algorithms, physical and network infrastructure, safety, standards and regulations, and end-user education. Unfortunately, technological advancement is outpacing the regulatory space and competition is driving deployment. Moreover, hope is being built around algorithms that are far from reaching human-like capacities on the road. Since human behaviors and idiosyncrasies and natural phenomena are not going anywhere anytime soon and so-called edge cases are the roadway norm, the industry stands at a historic crossroads. Why? Because human factors such as cognitive and behavioral insights into how we think, feel, act, plan, make decisions, and problem-solve have been ignored. Human cognitive intelligence is foundational to driving the industry’s ambition forward. In this paper I discuss the role of the human in bridging the gaps between autonomous vehicle technology, design, implementation, and beyond.

scholarly journals USING DEEP LEARNING TO DIGITIZE ROAD ARROW MARKINGS FROM LIDAR POINT CLOUD DERIVED IMAGES

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 123-129
Author(s):  
M. L. R. Lagahit ◽  
Y. H. Tseng
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Semantic Information ◽  
High Definition ◽  
The Road ◽  
Learning Techniques ◽  
Local Coordinates ◽  
On The Road ◽  
Self Driving Cars

Abstract. The concept of Autonomous vehicles or self-driving cars has recently been gaining a lot of popularity. Because of this, a lot of research is being done to develop the technology. One of which is High Definition (HD) Maps, which are centimeter-level precision 3D maps that contain a lot of geometric and semantic information about the road which can assist the AV when driving. An important component of HD maps is the road markings which indicates a set of rules on how a vehicle should navigate itself on the road. For example, lane lines indicate which part of the road a vehicle can drive on in a certain direction. This research proposes a methodology that uses deep learning techniques to detect road arrows, road markings that show possible driving directions, on LIDAR derived images, and extract them as polyline vector shapefiles. The general workflow consists of (1) converting the LIDAR point cloud to images, (2) training and applying U-Net – a fully convolutional neural network, (3) creating masks from image segmentation results that have been transformed to fit the local coordinates, (4) extracting the polygons and polylines, and finally (5) exporting the vectors in shapefile format. The proposed methodology has shown promising results with object segmentation accuracies comparable with previous related works.

scholarly journals To perform road signs recognition for autonomous vehicles using cascaded deep learning pipeline

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Riadh Ayachi ◽  
Yahia ElFahem Said ◽  
Mohamed Atri
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
High Performance ◽  
Autonomous Vehicle ◽  
Sensory Information ◽  
Learning Model ◽  
The Road ◽  
Road Signs ◽  
Deep Learning Model

Autonomous vehicle is a vehicle that can guide itself without human conduction. It is capable of sensing its environment and moving with little or no human input. This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving. Advanced artificial intelligence control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant road signs. In this paper, we introduce an intelligent road signs classifier to help autonomous vehicles to recognize and understand road signs. The road signs classifier will be based on an artificial intelligence technique. In particular, a deep learning model is used, Convolutional Neural Networks (CNN). CNN is a widely used Deep Learning model to solve pattern recognition problems like image classification and object detection. CNN has been successfully used to solve computer vision problems because of its methodology in processing images which is similar to the human brain decision making. The evaluation of the proposed pipeline is proved using two different datasets. The proposed CNNs achieved high performance in road sign classification with a validation accuracy of 99.8% and a testing accuracy of 99.6%. The proposed method can be easily implemented for real-time application.

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