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... the bogie to post crash test, the Tx antenna was affixed 1 m behind the bumper of the bogie (Fig. 4a) at a height of 0.90 m (Fig. 4b). The Tx antenna was attached to an embedded USRP E312 with a coaxial cable (with 8 dB return loss). The Rx antenna was installed 10 m behind the crash impact point with a height of 0.90 m (Fig. ...
Context 2
... the bogie to post crash test, the Tx antenna was affixed 1 m behind the bumper of the bogie (Fig. 4a) at a height of 0.90 m (Fig. 4b). The Tx antenna was attached to an embedded USRP E312 with a coaxial cable (with 8 dB return loss). The Rx antenna was installed 10 m behind the crash impact point with a height of 0.90 m (Fig. ...
Context 3
... the bogie to post crash test, the Tx antenna was affixed 1 m behind the bumper of the bogie (Fig. 4a) at a height of 0.90 m (Fig. 4b). The Tx antenna was attached to an embedded USRP E312 with a coaxial cable (with 8 dB return loss). The Rx antenna was installed 10 m behind the crash impact point with a height of 0.90 m (Fig. ...
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... However, these solutions mainly focus on multi-vehicular crashes on roads and do not consider the single-vehicle RoR crashes, which result in more than half of the traffic fatalities. To reduce the losses caused by single-vehicle crashes, we introduced a new vehicular communication paradigm, vehicle-to-barrier (V2B) communications [17], for moving vehicles to establish a communication link with roadside barriers (Fig. 1). This link aims to exchange information (e.g., road condition, barrier type, curvature, road type, etc) between the moving vehicle and the barrier to assist the onboard computer or the driver so that a RoR crash can be avoided. ...
Vehicle-to-barrier (V2B) communications is an emerging communication technology between vehicles and road-side barriers to mitigate run-off-road crashes, which result in more than half of the traffic-related fatalities in the United States. To ensure V2B connectivity, establishing a reliable V2B channel is necessary before a potential crash, such that real-time information from barriers can help (semi-)autonomous vehicles make informed decisions. However, the characteristics of the V2B channel are not yet well understood. Therefore, in this paper, aV2B channel model is developed with three channel metrics: received power, root mean square (RMS) delay spread, and RMS Doppler spread based on experiments during controlled vehicle crash tests. Experimentation, empirical analyses, and mathematical models are introduced to capture the impacts of antenna height, barrier type, and vehicle type in V2B channel characteristics. Vehicle-height barrier antennas experience 6.4% (540ns) less reference delay spread while encountering 10% (13Hz) higher reference Doppler spread and 10dB more received power than the barrier-height barrier antennas. Moreover, steel barrier deployment results in a 21% (2, 040ns) larger reference delay spread and 2.4% (2.35Hz) smaller reference Doppler spread than concrete barrier deployment. Finally, the impact of the crash in the communication channel is investigated with these empirical metrics. To the best of our knowledge, this is the first V2B communication channel model that captures received power, RMS delay spread, and RMS Doppler spread validated with the most extensive set of vehicular crash tests. The experimental code and experiment dataset are made public to support reproducible research (https://github.com/UNL-CPN-Lab/Crashing-Waves).
... One of the key highlights of the IoV is its interactive model ( Figure 2) that includes V2V [29][30][31], V2R [32], V2S, V2M, and V2I. The IoV implementation requires different devices such as vehicles, portable gadgets, RSUs, sensors, and actuators, to serve as fundamental necessity for ITS applications. ...
The amalgamation of Vehicular Ad hoc Network (VANET) with the Internet of Things (IoT) leads to the concept of the Internet of Vehicles (IoV). IoV forms a solid backbone for Intelligent Transportation Systems (ITS), which paves the way for technologies that better explain about traffic efficiency and their management applications. IoV architecture is seen as a big player in different areas such as the automobile industry, research organizations, smart cities and intelligent transportation for various commercial and scientific applications. However, as VANET is vulnerable to various types of security attacks, the IoV structure should ensure security and efficient performance for vehicular communications. To address these issues, in this article, an authentication-based protocol (A-MAC) for smart vehicular communication is proposed along with a novel framework towards an IoV architecture model. The scheme requires hash operations and uses cryptographic concepts to transfer messages between vehicles to maintain the required security. Performance evaluation helps analyzing its strength in withstanding various types of security attacks. Simulation results demonstrate that A-MAC outshines other protocols in terms of communication cost, execution time, storage cost, and overhead.
... critical to achieve a high LoA [3]- [6]. However, with AV's high mobility and the associated dynamic environment, these communication requirements bring significant challenges to the cellular infrastructure [7]- [9]. ...
Autonomous vehicles (AV) is an advanced technology that can bring convenience, improve the road-network throughput, and reduce traffic accidents. To enable higher levels of automation (LoA), massive amounts of sensory data need to be uploaded to the network for processing, and then, maneuvering decisions must be returned to the AV. Furthermore, passengers might have a higher transmission rate demands for various data-hungry and delay-sensitive applications.
... Table I shows the classification of IoV research for smart cities which also gives a concise summary of the overall scope of the paper. ITS-based IoV applications [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39] Smart cities IoV applications [11], [40], [41], [42], [96], [97], [98], [99], [100], [101], [102] Architectures and Vehicle Interaction Models Layer architectures [20], [21], [22], [23], [24], [25], [26], [27], [28] Vehicle-to-Vehicle (V2V) [27], [43], [44], [45], [46], [47] Vehicle-to-Roadside (V2R) [48], [49], [50], [51], [52], [53], [54] Vehicle-to-Infrastructure (V2I) [45], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64] Vehicle-to-Building (V2B) [51], [65] Vehicle-to-Home (V2H) [66] Vehicle-to-Everything [44], [67], [68], [69] Vehicle-to-Grid (V2G) [70], [71], [72], [73], [74], [75] Vehicle-to-Pedestrian [76], [77], [78] Challenges ...
... Table I shows the classification of IoV research for smart cities which also gives a concise summary of the overall scope of the paper. ITS-based IoV applications [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39] Smart cities IoV applications [11], [40], [41], [42], [96], [97], [98], [99], [100], [101], [102] Architectures and Vehicle Interaction Models Layer architectures [20], [21], [22], [23], [24], [25], [26], [27], [28] Vehicle-to-Vehicle (V2V) [27], [43], [44], [45], [46], [47] Vehicle-to-Roadside (V2R) [48], [49], [50], [51], [52], [53], [54] Vehicle-to-Infrastructure (V2I) [45], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64] Vehicle-to-Building (V2B) [51], [65] Vehicle-to-Home (V2H) [66] Vehicle-to-Everything [44], [67], [68], [69] Vehicle-to-Grid (V2G) [70], [71], [72], [73], [74], [75] Vehicle-to-Pedestrian [76], [77], [78] Challenges ...
... Wireless LAN [76], LED projection [77], protection of road users [78] Vehicle-to-Roadside (V2R) BSM broadcast [48], rapid response safety [51], SD-IoV [52], data integrity [53] & streaming [54] Vehicle-to-Infrastructure (V2I) VVLN network [45], Social IoV [56], mobile content delivery network [57], resource allocation fairness [58], ZigBee [59], other technologies [60]- [63], multi-hop connectivity [64] Vehicle-to-Barrier (V2B) V2B communication systems [51] Vehicle-to-Home (V2H) V2H backup power outage [66] Vehicle-to-Everything (V2X) Optimal information dissemination [44], LTE-based [67], MIMO [68], service flow [69] Vehicle-to-Grid (V2G) Benefits to power systems and PEVs [70]- [75] C. IoV Intra-Vehicle Interaction Communication Models Intra-vehicle interaction models perform communications within the vehicle itself. This section gives an overview and current research for intra-vehicle communication models. ...
The Internet of Vehicles (IoV) is a convergence of the mobile Internet and the Internet of Things (IoT) where vehicles function as smart moving intelligent nodes or objects within the sensing network. This paper gives two contributions to the state-of-the-art for IoV technology research. First, we present a comprehensive review of the current and emerging IoV paradigms and communication models with an emphasis on deployment in smart cities. Currently, surveys from many authors have focused concentration on the IoV as only serving applications for intelligent transportation like driver safety, traffic efficiency, and infotainment. This paper presents a more inclusive review of the IoV for also serving the needs of smart cities for large-scale data sensing, collection, information processing, and storage. The second component of the paper presents a new universal architecture for the IoV which can be used for different communication models in smart cities to address the above challenges. It consists of seven layers which are Vehicle Identification Layer, Object Layer, Inter-Intra Devices Layer, Communication Layer, Servers and Cloud Services Layer, Big Data and Multimedia Computation Layer, and Application Layer. The final part of the paper discusses various challenges and gives some experimental results and insights for future research direction such as the effects of a large and growing number of vehicles and the packet delivery success rate in the dynamic network structure in a smart city scenario.
... Recent vehicles are equipped with sensory technologies, such as blind-spot detection or lane-departure warning. Yet, recent statistics released by the White House and U.S. Department of Transportation's National Highway Traffic Safety Administration show that 8.3% (2, 483) more people died in [7] traffic-related accidents in 2015 than in 2014, and this increasing trend continued in 2016 with 5.8% (1, 900) more fatalities compared to 2015 [5]. This unfortunate data point breaks a recent historical trend of fewer deaths occurring per year [6]. ...
... In almost all these cases, the V2I infrastructure are much higher as compared to traditional roadside barriers [37]. On the other hand, we show that V2B communications may benefit from a barrier-height deployment [7]. There is a significant improvement on signal strength when the receiver antennas are deployed on lower heights (0.82 m -barrier height and 1.4 m -roof-top of a sedan car height) than higher ones (1.9 m -rooftop of a SUV car and 3 m -traffic signal height). ...
... The header includes a 16-bit counter that is decreased by 1 every frame. The payload is only 1-byte with value of p = mod(k, 256), repeated at 7 locations (7,9,11,18,32,39, 67) out of the 138 bytes. The remaining 129 bytes have constant values and are all treated as pilots. ...
Vehicle-to-barrier (V2B) communication is expected to facilitate wireless interactions between vehicles and roadside barriers in next-generation intelligent transportation systems. V2B systems will help mitigate single-vehicle, run-off-road crashes, which account for more than 50% of roadside crash fatalities. In this work, the characteristics of the wireless channel prior to and during a crash are analyzed using orthogonal frequency division multiplexing (OFDM) techniques, which has been used in existing vehicular communication systems. More specifically, the performance of OFDM-based V2B links are measured in real-world crash tests for the first time. Three crash tests conducted at the Midwest Roadside Safety Facility, Lincoln, Nebraska, are reported: a bogie vehicle crashing into a soil-embedded post at 27 mph, a sedan crashing to a concrete curb at 15 mph, and a pickup crashing to a steel barrier at 62 mph. Metrics including signal to interference plus noise ratio received signal strength, error vector magnitude, phase error, channel coherence, and bit error rate, are used to illustrate the impacts of antenna type, antenna deployment, speed, and mobility during the crash tests. The empirical evidence shows that barrier-height (0.7–0.9 m) antennas at the barrier can improve V2B signal quality compared to higher deployments (≥1.5 m) due to the stronger reflection of electromagnetic waves at a larger angle of incidence. Moreover, compared to omni-directional barrier antennas, directional barrier antennas can increase signal quality, connectivity, and coherence time of V2B channel because of reduced multi-path effects, however, the antenna orientation needs to be carefully determined to maintain connectivity.
... The ∼ 5dB increase in EVM in pre- 2) Brake Tests at 2.4 GHz: For each of the three phases, we analyzed the signal impairments (EVM and PE) mainly caused by the barrier and vehicle brake. The average results are summarized in Table II and detailed results can be found in [19]. During a peri-break interval, Rx antenna on the barrier (0.82 m) results in nearly 3 dB increase in EVM compared to behind the barrier deployment (1.4 m). ...
Vehicle-to-barrier (V2B) communication is a recently introduced vehicular communication technology, which aims to facilitate wireless interactions between vehicles and roadside barriers in next-generation vehicular systems. V2B systems will help mitigate single-vehicle, run-off-road (RoR) crashes, which account for a large proportion of roadside crash fatalities. RoR crashes may not be addressed by existing vehicular communication systems, such as vehicle-to-vehicle and vehicle-to-infrastructure. Today, orthogonal frequency division multiplexing (OFDM) is vastly utilized in vehicular communication systems. Thus, there is a need to understand the signal characteristics of the channel, especially just before and during a crash. To this end, the first real-world crash test measurement results for OFDM-based V2B communications are presented herein based on two crash tests. These tests include a bogie vehicle crash test into a soil embedded post at an impact velocity of 27 mph and a Toyota sedan crash test into a concrete curb with an impact velocity of 15 mph, conducted at the outdoor proving grounds of Midwest Roadside Safety Facility (MwRSF), Lincoln, Nebraska.
Experiment results illustrate the characteristics of V2B OFDM communication during vehicle encroachment and crash. The results highlight the adverse effects of vehicle encroachment and crash on OFDM signals, in terms of average received signal strength, peak to average power ratio, error vector magnitude, and phase error.
Navigating through roadworks represents one of the main sources of safety risk for Connected and Autonomous Vehicles (CAVs) due to the altered road layouts. The built-in base maps do not normally reflect these changes, causing CAVs to experience difficulties in sensing and trajectory generation. Therefore, the objective of this paper is to evaluate different collision-free trajectory generation for CAVs at roadworks to improve safety and traffic performance. Trajectory generation algorithms using lane-level dynamic maps were examined for: 1) CAVs rely on data from in-vehicle sensor only; and 2) CAVs receive additional information via a Smart Traffic Cone (STC) in advance regarding roadwork configurations. Experiments were conducted at a controlled motorway facility operated by National Highways (England) using a vehicle instrumented with a suite of sensors. Schematics of the roadworks scenario were translated into an integrated simulation platform consisting of a traffic microsimulation (VISSIM) to simulate traffic dynamics and a sub-microscopic simulator (PreScan) capable of simulating vehicle autonomy and connectivity. Results indicate that traffic conflicts and delays decrease by 40% and 3% respectively when CAVs receive additional information in advance (i.e., Scenario 2) compared to the other scenario. These findings would assist road network operators in developing ‘CAV-enabled roadworks’ and vehicle manufacturers in designing a vehicle-based ‘roadworks assist’ system.
This chapter discusses intelligent transport systems, which play a vital role in the lives of inhabitants living in large urban areas. These systems enable the mobility of vehicles but also of the people residing in cities. The issue of mobility in urban environments is highly relevant to them. In the context of the Internet of Things (IoT), vehicles transform into intelligent moving nodes or objects within the sensor network, combining the mobile internet and the Internet of Vehicles (IoV). With a focus on deployment in smart cities, this chapter examines the current and emerging paradigms and communication models of IoV. Many authors have surveyed IoV applications, including driver safety, traffic efficiency, and infotainment. The purpose is to assess the ability of the IoV to meet the data collection, processing, and storage needs of smart cities on a large scale.
With the increasing demand for advanced autonomous driving, the available communication resources may become constrained over different geographic areas. In addition, due to dynamic channel variations and imperfect cell deployments, guaranteeing the required communication resources for data hungry and delay-sensitive applications in autonomous vehicles (AVs), along their entire trips, becomes challenging. To address these issues, the paper investigates the feasibility of a hybrid system-optimum and user-equilibrium AV traffic framework subject to communication constraints, as well as its performance gain. Within such a framework, the paper introduces the problems of communication-constrained routing (CCR) and traffic control (CCTC) in the context of infrastructure-assisted autonomous driving and presents respective solutions. For CCR, an efficient two-layered routing scheme is proposed which can provide optimal trip duration. Simulation results show that the routing scheme achieves a good balance between longer duration of communication coverage and acceptable source-to-destination travel time. For CCTC, it is shown that there exists an optimal AV speed on each road segment, as well as an optimal inter-AV distance and an optimal number of AVs in each cell, to maximize the road-network AV throughput within a single cell. Moreover, spectrum allocation is used to achieve Pareto-optimal road-network throughput across cells, and a new key performance index (KPI) is defined to evaluate the traffic control capability of cellular systems. Simulation results validate the improvement of AV throughput via the proposed CCTC solution.
A new roadmap framework is proposed to improve the guidance and trajectory prediction capabilities of connected and automated vehicles (CAVs). Independent of road shape determination through external sensors, the system serves as a backup for challenging conditions, such as low sensor visibility and adverse environmental effects (e.g., rain, fog, snow). Based on the fusion of vehicle dynamics principles, differential geometry, and road design standards, the roadmap framework provides a consolidated collection of critical reference points of roadway centerlines and information about the shape of the roadway in the vicinity of a vehicle, including curvature, optimal travel velocity, and road alignment angle. Finally, the proposed roadmap for CAV reference offers versatility as additional data can be appended to the map, including elevation and roadside slope data, variable speed limits, and lane controls. Advisor: Cody S. Stolle
