Safe Intelligent Driver Assistance System in V2X Communication Environments based on IoT

In the modern world, power and speed of cars have increased steadily, as traffic continued to increase. At the same time highway-related fatalities and injuries due to road incidents are constantly growing and safety problems come first. Therefore, the development of Driver Assistance Systems (DAS) has become a major issue. Numerous innovations, systems and technologies have been developed in order to improve road transportation and safety. Modern computer vision algorithms enable cars to understand the road environment with low miss rates. A number of Intelligent Transportation Systems (ITSs), Vehicle Ad-Hoc Networks (VANETs) have been applied in the different cities over the world. Recently, a new global paradigm, known as the Internet of Things (IoT) brings new idea to update the existing solutions. Vehicle-to-Infrastructure communication based on IoT technologies would be a next step in intelligent transportation for the future Internet-of-Vehicles (IoV). The overall purpose of this research was to come up with a scalable IoT solution for driver assistance, which allows to combine safety relevant information for a driver from different types of in-vehicle sensors, in-vehicle DAS, vehicle networks and driver`s gadgets. This study brushed up on the evolution and state-of-the-art of Vehicle Systems. Existing ITSs, VANETs and DASs were evaluated in the research. The study proposed a design approach for the future development of transport systems applying IoT paradigm to the transport safety applications in order to enable driver assistance become part of Internet of Vehicles (IoV). The research proposed the architecture of the Safe Intelligent DAS (SiDAS) based on IoT V2X communications in order to combine different types of data from different available devices and vehicle systems. The research proposed IoT ARM structure for SiDAS, data flow diagrams, protocols. The study proposes several IoT system structures for the vehicle-pedestrian and vehicle-vehicle collision prediction as case studies for the flexible SiDAS framework architecture. The research has demonstrated the significant increase in driver situation awareness by using IoT SiDAS, especially in NLOS conditions. Moreover, the time analysis, taking into account IoT, Cloud, LTE and DSRS latency, has been provided for different collision scenarios, in order to evaluate the overall system latency and ensure applicability for real-time driver emergency notification. Experimental results demonstrate that the proposed SiDAS improves traffic safety

Secure Vehicular Communication Systems: Implementation, Performance, and Research Challenges

Vehicular Communication (VC) systems are on the verge of practical deployment. Nonetheless, their security and privacy protection is one of the problems that have been addressed only recently. In order to show the feasibility of secure VC, certain implementations are required. In [1] we discuss the design of a VC security system that has emerged as a result of the European SeVeCom project. In this second paper, we discuss various issues related to the implementation and deployment aspects of secure VC systems. Moreover, we provide an outlook on open security research issues that will arise as VC systems develop from today's simple prototypes to full-fledged systems

Infrastructure-Assisted Message Dissemination for Supporting Heterogeneous Driving Patterns

With the advances of Internet of Things technologies, individual vehicles can now exchange information to improve traffic safety, and some vehicles can further improve safety and efficiency by coordinating their mobility via cooperative driving. To facilitate these applications, many studies have been focused on the design of inter-vehicle message dissemination protocols. However, most existing designs either assume individual driving pattern or consider cooperative driving only. Moreover, few of them fully exploit infrastructures, such as cameras, sensors, and road-side units. In this paper, we address the design of message dissemination that supports heterogeneous driving patterns. Specifically, we first propose an infrastructure-assisted message dissemination framework that can utilize the capability of infrastructures. We then present a novel beacon scheduling algorithm that aims at guaranteeing the timely and reliable delivery of both periodic beacon messages for cooperative driving and event-triggered safety messages for individual driving. To evaluate the performance of the protocol, we develop both theoretical analysis and simulation experiments. Extensive numerical results confirm the effectiveness of the proposed protocol

Approximate reinforcement learning to control beaconing congestion in distributed networks

In vehicular communications, the increase of the channel load caused by excessive periodical messages (beacons) is an important aspect which must be controlled to ensure the appropriate operation of safety applications and driver-assistance systems. To date, the majority of congestion control solutions involve including additional information in the payload of the messages transmitted, which may jeopardize the appropriate operation of these control solutions when channel conditions are unfavorable, provoking packet losses. This study exploits the advantages of non-cooperative, distributed beaconing allocation, in which vehicles operate independently without requiring any costly road infrastructure. In particular, we formulate the beaconing rate control problem as a Markov Decision Process and solve it using approximate reinforcement learning to carry out optimal actions. Results obtained were compared with other traditional solutions, revealing that our approach, called SSFA, is able to keep a certain fraction of the channel capacity available, which guarantees the delivery of emergency-related notifications with faster convergence than other proposals. Moreover, good performance was obtained in terms of packet delivery and collision ratios.This research has been supported by the projects AIM, ref. TEC2016-76465-C2-1-R, ARISE2 “Future IoT Networks and Nano-networks (FINe)” ref. PID2020-116329GB-C22, ONOFRE-3, ref. PID2020-112675RB-C41 [Agencia Estatal de Investigación (AEI), European Regional Development Fund (FEDER), European Union (EU)], ATENTO, ref. 20889/PI/18 (Fundación Séneca, Región de Murcia), and LIFE [Fondo SUPERA Covid-19, funded by Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC), Universidades Españolas and Banco Santander]. J.A.P. thanks the Spanish MECD for an FPI grant ref. BES-2017-081061. Finally, the authors acknowledge Laura Wettersten for her contribution in reviewing the grammar and spell of the manuscript

Edge-based Collision Avoidance for Vehicles and Vulnerable Users

Collision avoidance is one of the most promising applications for vehicular networks, dramatically improving the safety of the vehicles that support it. In this paper, we investigate how it can be extended to benefit vulnerable users, e.g., pedestrians and bicycles, equipped with a smartphone. We argue that, owing to the reduced capabilities of smartphones compared to vehicular on-board units, traditional distributed approaches are not viable, and that multi-access edge computing (MEC) support is needed. Thus, we propose a MEC-based collision avoidance system, discussing its architecture and evaluating its performance. We find that, thanks to MEC, we are able to extend the protection of collision avoidance, traditionally thought for vehicles, to vulnerable users without impacting its effectiveness or latency

Wireless Networking for Vehicle to Infrastructure Communication and Automatic Incident Detection

Vehicular wireless communication has recently generated wide interest in the area of wireless network research. Automatic Incident Detection (AID), which is the recent focus of research direction in Intelligent Transportation System (ITS), aims to increase road safety. These advances in technology enable traffic systems to use data collected from vehicles on the road to detect incidents. We develop an automatic incident detection method that has a significant active road safety application for alerting drivers about incidents and congestion. Our method for detecting traffic incidents in a highway scenario is based on the use of distance and time for changing lanes along with the vehicle speed change over time. Numerical results obtained from simulating our automatic incident detection technique suggest that our incident detection rate is higher than that of other techniques such as integrated technique. probabilistic technique and California Algorithm. We also propose a technique to maximize the number of vehicles aware of Road Side Units (RSUs) in order to enhance the accuracy of our AID technique. In our proposed Method. IEEE 802.11 standard is used at RSUs with multiple antennas to assign each lane a specific channel. To validate our proposed approach. we present both analytical and simulation scenarios. The empirical values which are obtained from both analytical and simulation results have been compared to show their consistency. Results indicate that the IEEE 802.11 standard with its beaconing mechanism can be successfully used for Vehicle to Infrastructure (V2I) communications

Context-based Pseudonym Changing Scheme for Vehicular Adhoc Networks

Vehicular adhoc networks allow vehicles to share their information for safety and traffic efficiency. However, sharing information may threaten the driver privacy because it includes spatiotemporal information and is broadcast publicly and periodically. In this paper, we propose a context-adaptive pseudonym changing scheme which lets a vehicle decide autonomously when to change its pseudonym and how long it should remain silent to ensure unlinkability. This scheme adapts dynamically based on the density of the surrounding traffic and the user privacy preferences. We employ a multi-target tracking algorithm to measure privacy in terms of traceability in realistic vehicle traces. We use Monte Carlo analysis to estimate the quality of service (QoS) of a forward collision warning application when vehicles apply this scheme. According to the experimental results, the proposed scheme provides a better compromise between traceability and QoS than a random silent period scheme.Comment: Extended version of a previous paper "K. Emara, W. Woerndl, and J. Schlichter, "Poster: Context-Adaptive User-Centric Privacy Scheme for VANET," in Proceedings of the 11th EAI International Conference on Security and Privacy in Communication Networks, SecureComm'15. Dallas, TX, USA: Springer, June 2015.

SDDV: scalable data dissemination in vehicular ad hoc networks

An important challenge in the domain of vehicular ad hoc networks (VANET) is the scalability of data dissemination. Under dense traffic conditions, the large number of communicating vehicles can easily result in a congested wireless channel. In that situation, delays and packet losses increase to a level where the VANET cannot be applied for road safety applications anymore. This paper introduces scalable data dissemination in vehicular ad hoc networks (SDDV), a holistic solution to this problem. It is composed of several techniques spread across the different layers of the protocol stack. Simulation results are presented that illustrate the severity of the scalability problem when applying common state-of-the-art techniques and parameters. Starting from such a baseline solution, optimization techniques are gradually added to SDDV until the scalability problem is entirely solved. Besides the performance evaluation based on simulations, the paper ends with an evaluation of the final SDDV configuration on real hardware. Experiments including 110 nodes are performed on the iMinds w-iLab.t wireless lab. The results of these experiments confirm the results obtained in the corresponding simulations