scholarly journals Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4527 ◽  
Author(s):  
Sergey Chuprov ◽  
Ilya Viksnin ◽  
Iuliia Kim ◽  
Egor Marinenkov ◽  
Maria Usova ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accidents ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Unmanned Vehicles ◽  
Technical Problems ◽  
Traffic Jam ◽  
Safety Assurance ◽  
Physical Simulations ◽  
Intersection Management

Crossroads are the main traffic jam generators in densely populated cities. Unmanned vehicles and intelligent transportation systems can significantly reduce congestion and improve road safety by eliminating the main cause of traffic accidents—the human factor. However, full confidence in their safety is necessary. This paper addresses the contextual data integrity problem, when an unmanned autonomous vehicle transmits incorrect data due to technical problems, or malicious attacks. We propose an approach based on trust and reputation that allows detecting vehicles transmitting bogus data. To verify the feasibility of the approach on practice, we conducted both software and physical simulations using the model of intersection and unmanned autonomous vehicle models. The simulation results show that the approach applied allows detecting vehicles with bogus data and excluding them from the group, thus increasing the safety of the intersection traversal by other vehicles.

Vision-based Autonomous Vehicle Recognition

2021 ◽  
Vol 54 (4) ◽  
pp. 1-37
Author(s):  
Azzedine Boukerche ◽  
Xiren Ma
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Comprehensive Evaluation ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Regional Security ◽  
Future Research ◽  
Learning Abilities ◽  
Vehicle Recognition ◽  
Model Recognition

Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention recently. Particularly given the reliance on emerging deep learning methods, which have powerful feature extraction and pattern learning abilities, vehicle recognition has made significant progress. VAVR is an essential part of Intelligent Transportation Systems. The VAVR system can fast and accurately locate a target vehicle, which significantly helps improve regional security. A comprehensive VAVR system contains three components: Vehicle Detection (VD), Vehicle Make and Model Recognition (VMMR), and Vehicle Re-identification (VRe-ID). These components perform coarse-to-fine recognition tasks in three steps. In this article, we conduct a thorough review and comparison of the state-of-the-art deep learning--based models proposed for VAVR. We present a detailed introduction to different vehicle recognition datasets used for a comprehensive evaluation of the proposed models. We also critically discuss the major challenges and future research trends involved in each task. Finally, we summarize the characteristics of the methods for each task. Our comprehensive model analysis will help researchers that are interested in VD, VMMR, and VRe-ID and provide them with possible directions to solve current challenges and further improve the performance and robustness of models.

Fog-based Intelligent Transportation System for Traffic Light Optimization

2021 ◽  
Vol 58 (S) ◽  
pp. 29-35
Author(s):  
Muhammad Rusyadi Ramli ◽  
Riesa Krisna Astuti Sakir ◽  
Dong-Seong Kim
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accidents ◽  
Intelligent Transportation System ◽  
Optimization Problems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Light ◽  
Traffic Condition ◽  
Dilemma Zone ◽  
Traffic Lights

This paper presents fog-based intelligent transportation systems (ITS) architecture for traffic light optimization. Specifically, each intersection consists of traffic lights equipped with a fog node. The roadside unit (RSU) node is deployed to monitor the traffic condition and transmit it to the fog node. The traffic light center (TLC) is used to collect the traffic condition from the fog nodes of all intersections. In this work, two traffic light optimization problems are addressed where each problem will be processed either on fog node or TLC according to their requirements. First, the high latency for the vehicle to decide the dilemma zone is addressed. In the dilemma zone, the vehicle may hesitate whether to accelerate or decelerate that can lead to traffic accidents if the decision is not taken quickly. This first problem is processed on the fog node since it requires a real-time process to accomplish. Second, the proposed architecture aims each intersection aware of its adjacent traffic condition. Thus, the TLC is used to estimate the total incoming number of vehicles based on the gathered information from all fog nodes of each intersection. The results show that the proposed fog-based ITS architecture has better performance in terms of network latency compared to the existing solution in which relies only on TLC.

scholarly journals Deep Learning System for Vehicular Re-Routing and Congestion Avoidance

2019 ◽  
Vol 9 (13) ◽  
pp. 2717 ◽  
Author(s):  
Pedro Perez-Murueta ◽  
Alfonso Gómez-Espinosa ◽  
Cesar Cardenas ◽  
Miguel Gonzalez-Mendoza
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Transportation Systems ◽  
Learning System ◽  
Traffic Network ◽  
Public And Private ◽  
Traffic Jam ◽  
Private Transportation ◽  
Alternative Routes

Delays in transportation due to congestion generated by public and private transportation are common in many urban areas of the world. To make transportation systems more efficient, intelligent transportation systems (ITS) are currently being developed. One of the objectives of ITS is to detect congested areas and redirect vehicles away from them. However, most existing approaches only react once the traffic jam has occurred and, therefore, the delay has already spread to more areas of the traffic network. We propose a vehicle redirection system to avoid congestion that uses a model based on deep learning to predict the future state of the traffic network. The model uses the information obtained from the previous step to determine the zones with possible congestion, and redirects the vehicles that are about to cross them. Alternative routes are generated using the entropy-balanced k Shortest Path algorithm (EBkSP). The proposal uses information obtained in real time by a set of probe cars to detect non-recurrent congestion. The results obtained from simulations in various scenarios have shown that the proposal is capable of reducing the average travel time (ATT) by up to 19%, benefiting a maximum of 38% of the vehicles.

scholarly journals Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Qiyi He ◽  
Xiaolin Meng ◽  
Rong Qu
Keyword(s):  
Cyber Security ◽  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Assessment Method ◽  
Transportation Systems ◽  
Cyber Attacks ◽  
Large Set ◽  
Security Attacks ◽  
Physical Damage

CAV (connected and autonomous vehicle) is a crucial part of intelligent transportation systems. CAVs utilize both sensors and communication components to make driving decisions. A large number of companies, research organizations, and governments have researched extensively on the development of CAVs. The increasing number of autonomous and connected functions however means that CAVs are exposed to more cyber security vulnerabilities. Unlike computer cyber security attacks, cyber attacks to CAVs could lead to not only information leakage but also physical damage. According to the UK CAV Cyber Security Principles, preventing CAVs from cyber security attacks need to be considered at the beginning of CAV development. In this paper, a large set of potential cyber attacks are collected and investigated from the aspects of target assets, risks, and consequences. Severity of each type of attacks is then analysed based on clearly defined new set of criteria. The levels of severity for the attacks can be categorized as critical, important, moderate, and minor. Mitigation methods including prevention, reduction, transference, acceptance, and contingency are then suggested. It is found that remote control, fake vision on cameras, hidden objects to LiDAR and Radar, spoofing attack to GNSS, and fake identity in cloud authority are the most dangerous and of the highest vulnerabilities in CAV cyber security.

scholarly journals TRAFFIC SIGN INVENTORY FROM GOOGLE STREET VIEW IMAGES

2016 ◽  
Vol XLI-B4 ◽  
pp. 243-246
Author(s):  
Victor J. D. Tsai ◽  
Jyun-Han Chen ◽  
Hsun-Sheng Huang
Keyword(s):  
Intelligent Transportation Systems ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Traffic Sign ◽  
Google Street View ◽  
Street View ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection and recognition (TSDR) has drawn considerable attention on developing intelligent transportation systems (ITS) and autonomous vehicle driving systems (AVDS) since 1980’s. Unlikely to the general TSDR systems that deal with real-time images captured by the in-vehicle cameras, this research aims on developing techniques for detecting, extracting, and positioning of traffic signs from Google Street View (GSV) images along user-selected routes for low-cost, volumetric and quick establishment of the traffic sign infrastructural database that may be associated with Google Maps. The framework and techniques employed in the proposed system are described.

scholarly journals A Novel Adaptive Approach for Autonomous Vehicle Based on Optimization Technique for Enhancing the Communication between Autonomous Vehicle-to-Everything through Cooperative Communication

2021 ◽  
Vol 11 (19) ◽  
pp. 9089
Author(s):  
Radwa Ahmed Osman ◽  
Ahmed Kadry Abdelsalam
Keyword(s):  
Cooperative Communication ◽  
Intelligent Transportation Systems ◽  
Autonomous Vehicle ◽  
Optimization Technique ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
Emergency Vehicle ◽  
System Quality ◽  
Vehicular Communication ◽  
Proposed Model

Recent autonomous intelligent transportation systems commonly adopt vehicular communication. Efficient communication between autonomous vehicles-to-everything (AV2X) is mandatory to ensure road safety by decreasing traffic jamming, approaching emergency vehicle warning, and assisting in low visibility traffic. In this paper, a new adaptive AV2X model, based on a novel optimization method to enhance the connectivity of the vehicular networks, is proposed. The presented model optimizes the inter-vehicle position to communicate with the autonomous vehicle (AV) or to relay information to everything. Based on the system quality-of-service (QoS) being achieved, a decision will be taken whether the transmitting AV communicates directly to the destination or through cooperative communication. To achieve the given objectives, the best position of the relay-vehicle issue was mathematically formulated as a constrained optimization problem to enhance the communication between AV2X under different environmental conditions. To illustrate the effectiveness of the proposed model, the following factors are considered: distribution of vehicles, vehicle density, vehicle mobility and speed. Simulation results show how the proposed model outperforms other previous models and enhances system performance in terms of four benchmark aspects: throughput (S), packet loss rate (PLR), packet delivery ratio (PDR) and average delivery latency (DL).

scholarly journals Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation

2021 ◽  
Vol 21 (3) ◽  
pp. 127-144
Author(s):  
Andranik S. Akopov ◽  
Levon A. Beklaryan ◽  
Armen L. Beklaryan
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Traffic Accidents ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Mutation Operators ◽  
Novel Approach ◽  
Real Coded Genetic Algorithm ◽  
Simulation Based ◽  
Power Mutation

Abstract This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel genetic algorithm. The system being developed uses GPUs for the implementation of a massive agent-based model of Autonomous Vehicle (AV) behaviour in an Artificial Multi-Connected Road Network (AMСRN) consisting of the “Manhattan Grid” and the “Circular Motion Area” that are crossed. A new parallel Real-Coded Genetic Algorithm with a Scalable Nonuniform Mutation (RCGA-SNUM) is developed. The proposed algorithm (RCGA-SNUM) has been examined with the use of known test instances and compared with parallel RCGAs used with other mutation operators (e.g., standard mutation, Power Mutation (PM), mutation with Dynamic Rates (DMR), Scalable Uniform Mutation (SUM), etc.). As a result, RCGA-SNUM demonstrates superiority in solving large-scale optimisation problems when decision variables have wide feasible ranges and multiple local extrema are observed. Following this, RCGA-SNUM is applied to minimising the number of potential traffic accidents in the AMСRN.

scholarly journals An Area Partitioning Approach to the Conflation of Road Networks with Highly Different Level of Details

2021 ◽  
Author(s):  
Hoa-Hung Nguyen ◽  
Han-You Jeong
Keyword(s):  
Intelligent Transportation Systems ◽  
Road Network ◽  
Autonomous Vehicle ◽  
Road Networks ◽  
Autonomous Driving ◽  
Geographic Area ◽  
Transportation Systems ◽  
Route Guidance ◽  
Geospatial Information ◽  
Level Of Details

A road network represents road objects in a given geographic area and their interconnections, and is an essential component of intelligent transportation systems (ITS) enabling emerging new applications such as dynamic route guidance, driving assistance systems, and autonomous driving. As the digitization of geospatial information becomes prevalent, a number of road networks with a wide variety of characteristics coexist. In this paper, we present an area partitioning approach to the conflation of two road networks with a large difference in level of details. Our approach first partitions the geographic area by the Network Voronoi Area Diagram (NVAD) of low-detailed road network. Next, a subgraph of high-detailed road network corresponding to a complex intersection is extracted and then aggregated into a supernode so that a high matching precision can be achieved via 1:1 node matching. To improve the matching recall, we also present a few schemes that address the problem of missing corresponding object and representation dissimilarity between these road networks. Numerical results at Yeouido, Korea's autonomous vehicle testing site, show that our area partitioning approach can significantly improve the performance of road network matching.

A novel message authentication method for VANET without RSU

2016 ◽  
Vol 33 (8) ◽  
pp. 2288-2301 ◽  
Author(s):  
Alan Dahgwo Yein ◽  
Chih-Hsueh Lin ◽  
Yu-Hsiu Huang ◽  
Wen-Shyong Hsieh ◽  
Chung-Nan Lee ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accidents ◽  
Ad Hoc ◽  
Transportation Systems ◽  
Security Requirements ◽  
Message Authentication ◽  
Tier 2 ◽  
Content Type ◽  
Secret Keys ◽  
Trust Authority

Purpose Riding on the wave of intelligent transportation systems, the vehicular ad hoc network (VANET) is becoming a popular research topic. VANET is designed to build an environment where the vehicles can exchange information about the traffic conditions or vehicle situation to help the vehicles avoid traffic accidents or traffic jams. In order to keep the privacy of vehicles, the vehicles must be anonymous and the routing must be untraceable while still being able to be verified as legal entities. The paper aims to discuss these issues. Design/methodology/approach The exchanged messages must be authenticated to be genuine and verified that they were sent by a legal vehicle. The vehicles also can mutually trust and communicate confidentially. In VANETs, road-side units (RSUs) are installed to help the vehicles to obtain message authentication or communicate confidentially. However, the coverage of RSUs is limited due to the high cost of wide area installation. Therefore the vehicles must be able to obtain message authentication by themselves – without an RSU. Findings The authors take the concept of random key pre-distribution used in wireless sensor networks, modify it into a random secret pre-distribution, and integrate it with identity-based cryptography to make anonymous message authentication and private communication easier and safer. The authors construct a two-tier structure. The tier 1, trust authority, assigns n anonymous identities and embeds n secrets into these identities to be the private secret keys for the tier 2, registered vehicles. At any time, the vehicles can randomly choose one of n anonymous identities to obtain message authentication or communicate confidentially with other vehicles. Originality/value The processes of building neighbor set, setting pairing value, and message authenticating are proposed in this paper. The proposed method can protect against the attacks of compromising, masquerading, forging, and replying, and can also achieve the security requirements of VANET in message authentication, confidential communication, anonymity, and un-traceability. The performance of the proposed method is superior to the related works.

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