Developing a New Driver Assistance System for Overtaking on Two-Lane Roads using Predictive Models

The complexity of an overtaking maneuver on two-lane roads merits a thorough method for developing an assistance system to prevent accidents, thus reducing the number of fatalities and the associated economic costs. This research aims to introduce a new Driver Overtaking Assistance System (DOAS). This system is based on the proactive prediction of the possibility of overtaking any preceding vehicle(s) both accurately and safely. To provide a comprehensive system, different factors related to the driver, the vehicle, the road, and the environment which have an impact on the maneuver have been taken into consideration. In addition to considering the main overtaking strategies including accelerative, flying, piggybacking, and the 2+. The proposed system is a vehicle-based safety system based on the collection of contextual information from the driving vicinity through Hello beacon messages and a set of sensors that are used as part of the reasoning process of the context-aware architecture to safely initiate the overtaking maneuver. A classification model was implemented for both the Artificial Neural Network (ANN) and Support Vector Machine (SVM) learning algorithms. A vehicle driving simulator STISIM Drive® was used to conduct driving experiments for 100 participants of different ages, gender, and levels of mental awareness. The results obtained from the DOAS show high accuracy in aiding a safe overtaking maneuver. The classification model shows promising results in the predictions, through perfect accuracy and a very low level of outcome errors

Use Of Smartphones for Ensuring Vulnerable Road User Safety through Path Prediction and Early Warning: An In-Depth Review of Capabilities, Limitations and Their Applications in Cooperative Intelligent Transport Systems

The field of cooperative intelligent transport systems and more specifically pedestrians to vehicles could be characterized as quite challenging, since there is a broad research area to be studied, with direct positive results to society. Pedestrians to vehicles is a type of cooperative intelligent transport system, within the group of early warning collision/safety system. In this article, we examine the research and applications carried out so far within the field of pedestrians to vehicles cooperative transport systems by leveraging the information coming from vulnerable road users’ smartphones. Moreover, an extensive literature review has been carried out in the fields of vulnerable road users outdoor localisation via smartphones and vulnerable road users next step/movement prediction, which are closely related to pedestrian to vehicle applications and research. We identify gaps that exist in these fields that could be improved/extended/enhanced or newly developed, while we address future research objectives and methodologies that could support the improvement/development of those identified gaps

Security Management and Simulation for Mobile Ad hoc Networks

This chapter provides a detailed description of a framework for designing, analyzing, deploying, and enforcing high level security management for Mobile Ad Hoc Networks (MANETs). The framework, which can be used by researchers, academics, security administrators, network designers, and post-graduate students, is designed and simulated using the object oriented Network Simulator-2 (NS-2). In this chapter, the authors also provide a full illustration of how to design and implement a secure MANET, while maintaining the security essentials using NS-2. Then, they describe the characteristics, applications, design, coding style, advantages/disadvantages, and implementation of the NS-2 simulator. Finally, this chapter provides a description of the future trend NS-3, which is the “eventual replacement” of NS-2

Context-aware GPS integrity monitoring for intelligent transport systems

The full text of this article can be viewed via Open Access on the publishers website.The integrity of positioning systems has become an increasingly important requirement for location-based intelligent transport systems (ITS), for example electronic toll collection (ETC), public transport operations and traffic control services. In ITS, satellite navigation systems, such as global positioning system (GPS), are used to provide real-time vehicle positioning information including details of longitude, latitude, direction and speed. Map matching algorithms are used to integrate the positioning information into the digital road map. However, the navigation systems used in ITS cannot provide the high quality positioning information required by most services, due to the various types of errors made in the map matching process and experienced by GPS sensors such as signal outage, and errors due to atmospheric effects and receiver measurement errors, all of which are difficult to measure. An error in the positioning information or map matching process might lead to the inaccurate determination of a vehicle location. This could have legal or economic consequences for ITS applications such as traffic law enforcement systems (e.g., speed fining). Such applications require integrity when measuring the vehicle position and speed information and in the map matching process when locating the vehicle on the correct road segment to avoid errors when charging drivers. Consequently, the integrity algorithm for the navigation system should include a guarantee that the systems do not produce misleading or faulty information as this may lead to significant errors in the ITS services provided. In this paper, a high integrity GPS monitoring algorithm based on the concept of context-awareness that can be applied with real time ITS services to integrate changes in the integrity status of the navigation system was developed. Results suggest that the new integrity algorithm can support various types of location-based ITS services (e.g., route guidance)

Effect of roadway environment characteristics on pedestrian safety at signalised intersections in Amman

ABSTRACTPedestrian safety becoming a serious issue, especially in developing nations, wherein higher crash rates have been reported by the World Health Organization. Despite evidence suggesting higher pedestrian crash counts at signalised intersections in urban areas, there is a lack of in-depth analysis in most developing countries. Motivated by this need, this study aims to: 1) identify significant roadway environment characteristics and traffic volume factors influencing pedestrian – vehicle accidents at signalised intersections in Amman, Jordan, 2) elucidate relationships between pedestrian – vehicle accidents and these factors, and 3) discuss the limitations of pedestrian crash data and propose solutions for future research. We have analysed 166 accidents at 47 signalised intersections in Amman during the period of 2007–2019. The multilevel Generalised Linear Mixed Gamma regression model is the best fit for the data, indicating significant positive correlations between pedestrian crash frequencies and Annual Average Daily Traffic, pedestrian crossing volume, number of lanes, average lane width, and number of parking sides. Conversely, commercial land use and the presence of public transit facilities showed significant negative correlations with pedestrian crashes. This work presents a novel approach that will help developing countries to determine and explain pedestrian crash causes while considering various challenges in these contexts

Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs

Safety application systems in Vehicular Ad-hoc Networks (VANETs) require the dissemination of contextual information about the scale of neighbouring vehicles; therefore, ensuring security and privacy is of utmost importance. Vulnerabilities in the messages and the system’s infrastructure introduce the potential for attacks that lessen safety and weaken passengers’ privacy. The purpose of short-lived anonymous identities, called “pseudo-identities”, is to divide the trip into unlinkable short passages. Researchers have proposed changing pseudo-identities more frequently inside a pre-defined area, called a cryptographic mix-zone (CMIX) to ensure enhanced protection. According to ETSI ITS technical report recommendations, the researchers must consider the low-density scenarios to achieve unlinkability in CMIX. Recently, Christian et al. proposed a Chaff-based CMIX scheme that sends fake messages under the consideration of low-density conditions to enhance vehicles’ privacy and confuse attackers. To accomplish full unlinkability, in this paper, we first show the following security and privacy vulnerabilities in the Christian et al. scheme: Linkability attacks outside the CMIX may occur due to deterministic data sharing during the authentication phase (e.g., duplicate certificates for each communication). Adversaries may inject fake certificates, which breaks Cuckoo Filters’ (CFs) updates authenticity, and the injection may be deniable. CMIX symmetric key leakage outside the coverage may occur. We propose a VPKI-based protocol to mitigate these issues. First, we use a modified version of Wang et al.’s scheme to provide mutual authentication without revealing the real identity. To this end, the messages of a vehicle are signed with a different pseudo-identity “certificate”. Furthermore, the density is increased via the sending of fake messages in low traffic periods to provide unlinkability outside the mix-zone. Second, unlike Christian et al.’s scheme, we use the Adaptive Cuckoo Filter (ACF) instead of CF to overcome the false positives’ effect on the whole filter. Moreover, to prevent any alteration of the ACFs, only RUSs distribute the updates, and they sign the new fingerprints. Third, the mutual authentication prevents any leakage from the mix zones’ symmetric keys by generating a fresh one for each communication through a Diffie–Hellman key exchange

A Honeybee-Inspired Framework for a Smart City Free of Internet Scams

Internet scams are fraudulent attempts aim to lure computer users to reveal their credentials or redirect their connections to spoofed webpages rather than the actual ones. Users’ confidential information, such as usernames, passwords, and financial account numbers, is the main target of these fraudulent attempts. Internet scammers often use phishing attacks, which have no boundaries, since they could exceed hijacking conventional cyber ecosystems to hack intelligent systems, which emerged recently for the use within smart cities. This paper therefore develops a real-time framework inspired by the honeybee defense mechanism in nature for filtering phishing website attacks in smart cities. In particular, the proposed framework filters phishing websites through three main phases of investigation: PhishTank-Match (PM), Undesirable-Absent (UA), and Desirable-Present (DP) investigation phases. The PM phase is used at first in order to check whether the requested URL is listed in the blacklist of the PhishTank database. On the other hand, the UA phase is used for investigation and checking for the absence of undesirable symbols in uniform resource locators (URLs) of the requested website. Finally, the DP phase is used as another level of investigation in order to check for the presence of the requested URL in the desirable whitelist. The obtained results show that the proposed framework is deployable and capable of filtering various types of phishing website by maintaining a low rate of false alarms