Models and Performance of VANET based Emergency Braking

The network research community is working in the field of automotive to provide VANET based safety applications to reduce the number of accidents, deaths, injuries and loss of money. Several approaches are proposed and investigated in VANET literature, but in a completely network-oriented fashion. Most of them do not take into account application requirements and no one considers the dynamics of the vehicles. Moreover, message repropagation schemes are widely proposed without investigating their benefits and using very complicated approaches. This technical report, which is derived from the Master Thesis of Michele Segata, focuses on the Emergency Electronic Brake Lights (EEBL) safety application, meant to send warning messages in the case of an emergency brake, in particular performing a joint analysis of network requirements and provided application level benefits. The EEBL application is integrated within a Collaborative Adaptive Cruise Control (CACC) which uses network-provided information to automatically brake the car if the driver does not react to the warning. Moreover, an information aggregation scheme is proposed to analyze the benefits of repropagation together with the consequent increase of network load. This protocol is compared to a protocol without repropagation and to a rebroadcast protocol found in the literature (namely the weighted p-persistent rebroadcast). The scenario is a highway stretch in which a platoon of vehicles brake down to a complete stop. Simulations are performed using the NS_3 network simulation in which two mobility models have been embedded. The first one, which is called Intelligent Driver Model (IDM) emulates the behavior of a driver trying to reach a desired speed and braking when approaching vehicles in front. The second one (Minimizing Overall Braking Induced by Lane change (MOBIL)), instead, decides when a vehicle has to change lane in order to perform an overtake or optimize its path. The original simulator has been modified by - introducing real physical limits to naturally reproduce real crashes; - implementing a CACC; - implementing the driver reaction when a warning is received; - implementing different network protocols. The tests are performed in different situations, such as different number of lanes (one to five), different average speeds, different network protocols and different market penetration rates and they show that: - the adoption of this technology considerably decreases car accidents since the overall average maximum deceleration is reduced; - network load depends on application-level details, such as the implementation of the CACC; - VANET safety application can improve safety even with a partial market penetration rate; - message repropagation is important to reduce the risk of accidents when not all vehicles are equipped; - benefits are gained not only by equipped vehicles but also by unequipped ones

Secure Intelligent Vehicular Network Including Real-Time Detection of DoS Attacks in IEEE 802.11P Using Fog Computing

VANET (Vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. Intermittent exchange of real-time safety message delivery in VANET has become an urgent concern, due to DoS (Denial of service), and smart and normal intrusions (SNI) attacks. Intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication need. In this research, Fog computing (FC) integrates with hybrid optimization algorithms (OAs) including: Cuckoo search algorithm (CSA), Firefly algorithm (FA) and Firefly neural network, in addition to key distribution establishment (KDE), for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme which is also termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC) utilizes feedforward back propagation neural network (FFBP-NN). This is also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The proposed scheme is initially compared with the Cuckoo and FA, and the Firefly neural network to evaluate the QoS parameters such as jitter and throughput. In addition, VANET is a means whereby Intelligent Transportation System (ITS) has become important for the benefit of daily lives. Therefore, real-time detection of all form attacks including hybrid DoS attacks in IEEE 802.11p, has become an urgent attention for VANET. This is due to sporadic real-time exchange of safety and road emergency message delivery in VANET. Sporadic communication in VANET has the tendency to generate enormous amount of message. This leads to the RSU (roadside unit) or the CPU (central processing unit) overutilization for computation. Therefore, it is required that efficient storage and intelligence VANET infrastructure architecture (VIA), which include trustworthiness is desired. Vehicular Cloud and Fog Computing (VFC) play an important role in efficient storage, computations, and communication need for VANET. This dissertation also utilizes VFC integration with hybrid optimization algorithms (OAs), which also possess swarm intelligence including: Cuckoo/CSA Artificial Bee Colony (ABC) Firefly/Genetic Algorithm (GA), in additionally to provide Real-time Detection of DoS attacks in IEEE 802.11p, using VFC for Intelligent Vehicular network. Vehicles are moving with certain speed and the data is transmitted at 30Mbps. Firefly FFBPNN (Feed forward back propagation neural network) has been used as a classifier to also distinguish between the attacked vehicles and the genuine vehicle. The proposed scheme has also been compared with Cuckoo/CSA ABC and Firefly GA by considering Jitter, Throughput and Prediction accuracy

Rearward visibility issues related to agricultural machinery: Contributing factors, potential solutions

As the size, complexity, and speed of tractors and other agricultural self-propelled machinery have increased, so have the visibility-related issues, placing significant importance on the visual skills, alertness, and reactive abilities of the operator. Rearward movement of large agricultural equipment has been identified in the literature as causing not only damage to both machine and stationary objects, but also injuries (even fatalities) to bystanders not visible to the operator. Fortunately, monitoring assistance, while not a new concept, has advanced significantly, offering operators today more options for increasing awareness of the area surrounding their machines. In this research, an attempt is made to (1) identify and describe the key contributors to agricultural machinery visibility issues (both operator and machine-related), and (2) enumerate and evaluate the potential solutions and technologies that address these issues via modifications of ISO, SAE, and DOT standardized visibility testing methods. Enhanced operator safety and efficiency should result from a better understanding of the visibility problems (especially with regard to rearward movement) inherent in large tractors and self-propelled agricultural machinery. Used in this study were nine machines of different types that varied widely in size, horsepower rating, and operator station configuration to provide a broad representation of what is found on many U.S. farms/ranches. The two main rearward monitoring ‘technologies’ evaluated were the machines’ factory-equipped mirrors and cameras that the researchers affixed to these machines. A 58.06 m2 (625 ft2) testing grid was centered on the rear-most location of the tested machinery with height indicators centered in each of twenty-five grid cells. In general, the findings were consistent across all the machines tested—i.e., rather obstructed rearward visibility using mirrors alone versus considerably less obstructed rearward visibility with the addition of cameras. For example, having exterior extended-arm and interior mirrors only, a MFWD tractor with 1,100-bushel grain cart in tow measured, from the operator’s perspective, 68% obstructed view of the grid’s kneeling-worker-height markers and 100% throughout the midline of rearward travel; but when equipped with a rearview camera system, the obstructed area was decreased to only 4%. The visibility models created identified (1) a moderate-positive Pearson r correlation, indicating that many of the obstructed locations of the rearward area affected both mirrors and cameras similarly and (2) a strong-positive Pearson r correlation of kneeling worker height visibility, indicating that mirrors and camera systems share commonality of areas with high visibility (along the midline of travel and outward with greater distance from the rear of the machine, without implements in tow). Of the recommendations coming from this research, the key one is for establishment of engineering standards aimed at (1) enhancing operator ability to identify those locations around agricultural machinery that are obstructed from view, (2) reducing the risk of run-overs through improved monitoring capabilities of machine surroundings and components, and (3) alerting operators and co-workers of these hazardous locations

Assessing Pedestrian Safety Conditions on Campus

Pedestrian-related crashes are a significant safety issue in the United States and cause considerable amounts of deaths and economic cost. Pedestrian safety is an issue that must be uniquely evaluated in a college campus, where pedestrian volumes are dense. The objective of this research is to identify issues at specific locations around UCF and suggest solutions for improvement. To address this problem, a survey that identifies pedestrian safety issues and locations is distributed to UCF students and staff, and an evaluation of drivers reactions to pedestrian to vehicle (P2V) warning systems is studied through the use of a NADS MiniSim driving simulator. The survey asks participants to identify problem intersections around campus and other issues as pedestrians or bicyclists in the UCF area. Univariate probit models were created from the survey data to identify which factors contribute to pedestrian safety issues, based off the pedestrian\u27s POV and the driver\u27s POV. The models indicated that the more one is exposed to traffic via walking, biking, and driving to campus contributes to less safe experiences. The models also show that higher concerns with drivers not yielding, unsafety of crossing the intersections, and the number of locations to cross, indicate less safe pedestrian experiences from the point of view of pedestrians and drivers. A promising solution for pedestrian safety is Pedestrian to Vehicle (P2V) communication. This study simulates P2V connectivity using a NADS MiniSim Driving Simulator to study the effectiveness of the warning system on drivers. According to the results, the P2V warning system significantly reduced the number of crashes in the tested pre-crash scenarios by 88%. Particularly, the P2V warning system can help decrease the driver\u27s reaction time as well as impact velocity if the crash were to occur

A Comprehensive Approach to WSN-Based ITS Applications: A Survey

In order to perform sensing tasks, most current Intelligent Transportation Systems (ITS) rely on expensive sensors, which offer only limited functionality. A more recent trend consists of using Wireless Sensor Networks (WSN) for such purpose, which reduces the required investment and enables the development of new collaborative and intelligent applications that further contribute to improve both driving safety and traffic efficiency. This paper surveys the application of WSNs to such ITS scenarios, tackling the main issues that may arise when developing these systems. The paper is divided into sections which address different matters including vehicle detection and classification as well as the selection of appropriate communication protocols, network architecture, topology and some important design parameters. In addition, in line with the multiplicity of different technologies that take part in ITS, it does not consider WSNs just as stand-alone systems, but also as key components of heterogeneous systems cooperating along with other technologies employed in vehicular scenarios