Big data traffic management in vehicular ad-hoc network

Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety

Sensor Technologies for Intelligent Transportation Systems

Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment

Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning

International audienceUrban traffic forecasting models generally follow either a Gaussian Mixture Model (GMM) or a Support Vector Classifier (SVC) to estimate the features of potential road accidents. Although SVC can provide good performances with less data than GMM, it incurs a higher computational cost. This paper proposes a novel framework that combines the descriptive strength of the Gaussian Mixture Model with the high-performance classification capabilities of the Support Vector Classifier. A new approach is presented that uses the mean vectors obtained from the GMM model as input to the SVC. Experimental results show that the approach compares very favorably with baseline statistical methods

Real time collision warning system in the context of vehicle-to-vehicle data exchange based on drivings behaviours analysis

Worldwide injuries in vehicle accidents have been on the rise in recent years, mainly due to driver error regardless of technological innovations and advancements for vehicle safety. Consequently, there is a need for a reliable-real time warning system that can alert drivers of a potential collision. Vehicle-to-Vehicle (V2V) is an extensive area of ongoing research and development which has started to revolutionize the driving experience. Driving behaviour is a subject of extensive research which gains special attention due to the relationship between speeding behaviour and crashes as drivers who engage in frequent and extreme speeding behaviour are overinvolved in crashes. National Highway Traffic Safety Administration (NHTSA) set guidelines on how different vehicle automation levels may reduce vehicle crashes and how the use of on-board short-range sensors coupled with V2V technologies can help facilitate communication among vehicles. Based on the previous works, it can be seen that the assessment of drivers’ behaviours using their trajectory data is a fresh and open research field. Most studies related to driving behaviours in terms of acceleration�deceleration are evaluated at the laboratory scale using experimental results from actual vehicles. Towards this end, a five-stage methodology for a new collision warning system in the context of V2V based on driving behaviours has been designed. Real-time V2V hardware for data collection purposes was developed. Driving behaviour was analyzed in different timeframes prior obtained from actual driving behaviour in an urban environment collected from OBD-II adapter and GPS data logger of an instrumented vehicle. By measuring the in-vehicle accelerations, it is possible to categorize the driving behaviour into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. When the vehicle is in a risk situation, the system based on NRF24L01+PA/LNA, GPS, and OBD-II will pass a signal to the driver using a dedicated LCD and LED light signal. The driver can instantly decide to make the vehicle in a safe mood, effectively avoid the happening of vehicle accidents. The proposed solution provides two main functions: (1) the detection of the dangerous vehicles involved in the road, and (2) the display of a message informing the driver if it is safe or unsafe to pass. System performance was evaluated to ensure that it achieved the primary objective of improving road safety in the extreme behaviour of the driver in question either the safest (or the least aggressive) and the most unsafe (or the most aggressive). The proposed methodology has retained some advantages for other literature studies because of the simultaneous use of speed, acceleration, and vehicle location. The V2V based on driving behaviour experiments shows the effectiveness of the selected approach predicts behaviour with an accuracy of over 87% in sixty-four real-time scenarios presented its capability to detect behaviour and provide a warning to nearby drivers. The system failed detection only in few times when the receiving vehicle missed data due to high speed during the test as well as the distances between the moving vehicles, the data was not received correctly since the power transmitted, the frequency range of the signals, the antenna relative positions, and the number of in-range vehicles are of interest for the V2V test scenarios. The latter result supports the conclusion that warnings that efficiently and quickly transmit their information may be better when driver are under stress or time pressure

Machine Learning for Next-Generation Intelligent Transportation Systems: A Survey

International audienceIntelligent Transportation Systems, or ITS for short, includes a variety of services and applications such as road traffic management, traveler information systems, public transit system management, and autonomous vehicles, to name a few. It is expected that ITS will be an integral part of urban planning and future cities as it will contribute to improved road and traffic safety, transportation and transit efficiency, as well as to increased energy efficiency and reduced environmental pollution. On the other hand, ITS poses a variety of challenges due to its scalability and diverse quality-of-service needs, as well as the massive amounts of data it will generate. In this survey, we explore the use of Machine Learning (ML), which has recently gained significant traction, to enable ITS. We provide a comprehensive survey of the current state-of-the-art of how ML technology has been applied to a broad range of ITS applications and services, such as cooperative driving and road hazard warning, and identify future directions for how ITS can use and benefit from ML technology

Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms

Automotive technology has continued to advance in many aspects. As an outcome of such advancements, autonomous vehicles are closer to commercialization and have brought to life a complex automotive technology ecosystem [1]. Like every other technology, these developments bring benefits but also introduce a variety of risks. One of these risks in the automotive space is cybersecurity threats. In the case of cars, these security challenges can produce devastating results and tremendous costs, including loss of life. Therefore, conducting a clear analysis, assessment and detection of threats solves some of the cybersecurity challenges in the automotive ecosystem. This dissertation does just that, by building a three-step framework to analyze, assess,and detect threats using machine learning algorithms. First, it does an analysis of the connected vehicle threats while leveraging the STRIDE framework [2]. Second, it presents an innovative, Fuzzy based threat assessment model (FTAM). FTAM leverages threat characterizations from established threat assessment models while focusing on improving its assessment capabilities by using Fuzzy logic. Through this methodology, FTAM can improve the efficiency and accuracy of the threat assessment process by using Fuzzy logic to determine the “degree” of the threat over other existing methods. This differs from the current threat assessment models which use subjective assessment processes based on table look-ups or scoring. Thirdly, this dissertation proposes an intrusion detection system (IDS) to detect malicious threats while taking in consideration results from the previous assessment stage. This IDS uses the dataset provided from Wyoming Connected Vehicle Deployment program [3] and consists of a two-stage intrusion detection system based on supervised and unsupervised machine learning algorithms. The first stage uses unsupervised learning to detect whether there is an attack present and the second stage classifies these attacks in a supervised learning fashion. The second stage also addresses data bias and eliminates the number of false positives. The simulation of this approach results in an IDS able to detect and classify attacks at a 99.965% accuracy and lowers the false positives rate to 0%.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/149467/1/Nevrus Kaja PhD Dissertation V24.pdfDescription of Nevrus Kaja PhD Dissertation V24.pdf : Dissertatio

Physiological-based Driver Monitoring Systems: A Scoping Review

A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PD

A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G has enabled Ultra-Reliable Low Latency Communications (URLLC) to CAVs. However, while communication performance has been enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure (PKI) proposed by standardization bodies cannot solely defend against these attacks. Thus, in complementary of that, sophisticated systems should be designed to detect such attacks and attackers. Machine Learning (ML) has recently emerged as a key enabler to secure future roads. Various V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. However, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper comprehensively surveys and classifies ML-based MDSs as well as discusses and analyses them from security and ML perspectives. It also provides some learned lessons and recommendations for guiding the development, validation, and deployment of ML-based MDSs. Finally, this paper highlighted open research and standardization issues with some future directions

Vulnerable road users and connected autonomous vehicles interaction: a survey

There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.This work was partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under Grant: Supervision of drone fleet and optimization of commercial operations flight plans, PID2020-116377RB-C21.Peer ReviewedPostprint (published version