Vehicles In Scenarios Research Articles

Connectivity probability analysis for freeway vehicle scenarios in vehicular networks

The connectivity probability analysis of vehicular networks can be employed for providing theoretical guidance for both obtaining an accurate real-time traffic information and reducing hazardous traffic situations. Most previous studies focused on analyzing the connectivity probability of vehicular networks in physical (PHY) layer protocol. However, the effects of packet collision in media access control (MAC) layer on the connectivity probability of vehicular networks have been rarely studied, where MAC and PHY layers actually interact on each other. In this paper, some parameters are dynamically set and analyzed under consideration of the influence of MAC and PHY layers on the connectivity probability of vehicular networks. Numerical results are shown to be consistent with the proposed theoretical analysis.

Solar-driven integrated energy systems: State of the art and challenges

Solar energy is one of the most popular clean energy resources that can be fully utilized to date. The growing energy demand of modern society has spurred the technological advance of solar cells affording high power conversion efficiency and low-cost aspects. Nevertheless, the intrinsically intermittent nature and fluctuating output power of solar irradiation greatly restrain the practical uses of solar cells, especially in the scenarios of smart consumer electronics, electrical vehicles and hydrogen fuel production. With a collection of attractive features including favorable stability, durability and practicability, solar-driven integrated energy system that synergizes energy harvesting and storage offer a viable solution. This review summarizes the state-of-the-art knowledge in designing concepts, integrated configurations and overall performances of different types of solar-driven hybrid energy units. Particular attentions are paid to highlighting the practical application sectors enabled by those integrated energy systems. Future perspectives and key challenges in this emerging field are also covered at the end.

Effect of RSU Placement on Autonomous Vehicle V2I Scenarios

Edge computing has become a prominent computing strategy as mobile devices and Internet of Things (IoT) became popular in the last decade where cloud computing proved partly insufficient meeting the computational requirements of these devices/applications. Unlike cloud, edge computing can provide low latency in communication, high quality of service, and support for high mobility. Connected and autonomous vehicles scenarios can be considered as an important application field for edge computing as these are the key requirements to implement a vehicular network. In this paper, we aim to present a remedy to one of the crucial problems in vehicular networks: efficient RSU placement by addressing network coverage and computational demand. We propose an RSU placement framework for generating placement models based on traffic characteristics of a target area. Our work differs from previous studies in that we focus on both communication coverage and the computational demand aspects simultaneously. The proposed framework in this study can be used by infrastructure providers for designing an efficient RSU placement while building a smart city. Moreover, our work includes extending capabilities of a simulation framework designed for edge computing scenarios. To demonstrate the effectiveness of our proposal we evaluated the performance of various placement models in realistic settings.

Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection

Unsignalized intersection control is one of the most critical issues in intelligent transportation systems, which requires connected and automated vehicles to support more frequent information interaction and on-board computing. It is very promising to introduce reinforcement learning in the unsignalized intersection control. However, the existing multiagent reinforcement learning algorithms, such as multiagent deep deterministic policy gradient (MADDPG), hardly handle a dynamic number of vehicles, which cannot meet the need of the real road condition. Thus, this paper proposes a Cooperative MADDPG (CoMADDPG) for connected vehicles at unsignalized intersection to solve this problem. Firstly, the scenario of multiple vehicles passing through an unsignalized intersection is formulated as a multiagent reinforcement learning (RL) problem. Secondly, MADDPG is redefined to adapt to the dynamic quantity agents, where each vehicle selects reference vehicles to construct a partial stationary environment, which is necessary for RL. Thirdly, this paper incorporates a novel vehicle selection method, which projects the reference vehicles on a virtual lane and selects the largest impact vehicles to construct the environment. At last, an intersection simulation platform is developed to evaluate the proposed method. According to the simulation result, CoMADDPG can reduce average travel time by 39.28% compared with the other optimization-based methods, which indicates that CoMADDPG has an excellent prospect in dealing with the scenario of unsignalized intersection control.

Methodological Study of Evaluating Future Lightweight Vehicle Scenarios and CO2 Reduction Based on Life Cycle Assessment

Changing the material composition of vehicles from steel to alternative materials, such as aluminum and magnesium, is being explored to reduce the weight of vehicles. Further, this change could lead to a significant reduction in vehicular CO2 emissions. To analyze this relationship and estimate the CO2 reduction effect over the life cycle, it is important to create potential scenarios by considering the logistics balance from material production to recycling. Therefore, this study aims to quantitatively predict the amount of renewable energy employed in vehicles, along with the various alternative materials used; further, the demand for aluminum and magnesium is predicted. These predictions are made via several multivariate analyses and a dynamic substance flow analysis (SFA) to explore future scenarios. It is estimated that 65% of rolled aluminum can be obtained from a secondary alloy via closed-loop recycling of rolled products in a sustainable development scenario. However, 510 kt/year of end of life scrap aluminum must be imported from overseas to provide 90% of the secondary alloy required in cast and die cast parts. The overall CO2 reduction amount is predicted to be 3920 kt/year in the 2040 sustainable development scenario. This study successfully demonstrated that combining SFA and life cycle assessment is efficient for quantitatively estimating the synergies of renewable energy implementation, vehicular weight reduction, and recycling.

Traffic noise prediction model of an Indian road: an increased scenario of vehicles and honking.

Noise is considered as an underrated and underemphasized pollutant in contrast to other pollutants of the environment. Due to the non-acute response of health effects, people are not vigilant towards consequences regarding noise pollution. The expansion of the transportation industry is contributing towards the increment in the public and private vehicular volume which causes an increment in noise pollution. For evaluation of respective scenario, the research study has been conducted on one of the minor roads of Nagpur, India; for 2years, viz., 2012 and 2019. The study concludes an increment of 5-6dB(A) in noise level, 4-6 times in honking, and 1.7 times in traffic volume. The study confirms increment in sound pressure by 65.9% and 81.9% for the year 2012 and 2019 during morning and evening sessions, respectively. Noise prediction model has also been developed for the abovementioned years, using multiple regression analysis, considering traffic volume, honking, and speed against noise equivalent level. Honking has been further characterized into honk by light and medium category vehicles as acoustical properties of horns vary with respect to category of vehicle and introduced into the noise prediction model. Noise prediction model for 2019 has predicted the noise level in a range of - 1.7 to + 1.4dB (Leq) with 84% of observations in the range of - 1 to + 1dB (Leq), when compared with observed Leq on the field. For proper management of noise pollution, a noise prediction model is essentially needed so that the noise level can be anticipated, and accordingly, measures can be outlined and executed. This increased noise level has serious impacts on human hearing capacity and overall health. Accordingly, noise mitigation preventive measures are recommended to control traffic noise in the urban environment.

Two-Stages Carbon Emission Pinch Analysis for Integrated System of Renewable Energy and Electric Vehicle

The introduction of electric vehicles to the transportation fleet has merged the power generation and transportation sectors into an integrated system. Rather than fuel sources, electricity is used to charge electric vehicles, so these vehicles play a vital role as an important green technology that could reduce carbon emissions in the transportation sector. This study aimed to develop a multi-stage carbon emission pinch analysis for an integrated system to optimise the energy mix for electricity generation. In the first stage, the minimum number of electric vehicles required to reduce transportation emissions was determined. In the second stage, the optimal energy mix for power generation sector was determined while including the electricity demand for the electric vehicle. Four scenarios, namely the business-as-usual scenario (S1); the public transport utilization scenario (S2); the electrification of vehicle scenario (S3); and the Integrated-Policies scenario (S4) were developed based on Peninsular Malaysia as a case study, to analyse the impact of different mitigation strategies on the country’s economy. S1 was set as baseline for all the cases i.e., without any mitigation strategies. The results reveal that S4 was the best scenario, yielding a total cost saving percentage of 51.42 % compared to S1. For the power generation sector, 52 % of renewable energy (solar PV, biomass and biogas) utilisation would be needed in the energy mix to achieve the emission reduction target.

Probabilistic design fires for passenger vehicle scenarios

When considering fire safety design scenarios for car parking buildings it is possible that there will be spread of fire between multiple vehicles. Such scenarios need to account for the likely rate of heat release curve from already burning vehicles as this will impact on the likelihood that other vehicles will ignite and so increase the severity of the fire. Currently design fires for a single passenger vehicle are often either given as a specified rate of heat release history or as mathematical functions with fixed coefficients irrespective of the size (curb weight) of the vehicle. In this paper, experimental results taken from the literature are used to determine a probabilistic approach to characterise a single passenger vehicle design fire. It is found that a peak fire growth function and an exponential fire decay function gives an appropriate combination in which the parameters for the two functions can be determined from distribution statistics.

Uncertainty modeling and runtime verification for autonomous vehicles driving control: A machine learning-based approach

Intelligent Transportation Systems (ITS) are attracting much attention from the industry, academia, and government in staging the new generation of transportation. In the coming years, the human-driven vehicles and autonomous vehicles would co-exist for a long time in uncertain environments. How to efficiently control the autonomous vehicle and improve the interaction accuracy as well as the human drivers’ safety is a hot topic for the autonomous industry. The safety-critical nature of the ITSs demands the system designers to provide provably correct guarantees about the actions, models, control, and performance. To model and recognize the drivers’ behavior, we use machine learning classification algorithms based on the data we get from the uncertain environments. We define a parameterized modeling language stohChart(p) (parameterized stochastic hybrid statecharts) to describe the interactions of agents in ITSs. The learning result of the driver behavior classification is transferred to stohChart(p) as the parameters timely. Then we propose a mapping algorithm to transform stohChart(p) to NPTA (Networks of Probabilistic Timed Automata) and use the statistical model checker UPPAAL-SMC to verify the quantitative properties. So the run-time verification method can help autonomous vehicles make “more intelligent” decisions at run-time. We illustrate our approach by modeling and analyzing a scenario of the autonomous vehicle try to change to a lane occupied by a human-driven car.

An Application Development Framework for Internet-of-Things Service Orchestration

Application development for the Internet of Things (IoT) poses immense challenges due to the lack of standard development frameworks, tools, and techniques to assist end users in dealing with the complexity of IoT systems during application development. These challenges invoke the use of model-driven development (MDD) along with the representational state transfer (REST) architecture to develop IoT applications, supporting model generation at different abstraction levels while generating software implementation artifacts for heterogeneous platforms and ensuring loose coupling in complex IoT systems. This article proposes an IoT application development framework, named IADev, which uses attribute-driven design and MDD to address the above-mentioned challenges. This framework is composed of two major steps, including iterative architecture development using attribute-driven design and generating models to guide the transformation using MDD. IADev uses attribute-driven design to transform the requirements into a solution architecture by considering the concerns of all involved stakeholders, and then, MDD metamodels are generated to hierarchically transform the design components into the software artifacts. We evaluate IADev for a smart vehicle scenario in an intelligent transportation system to generate an executable implementation code for a real-world system. The case study experiments proclaim that IADev achieves higher satisfaction of the participants for the IoT application development and service orchestration, as compared to conventional approaches. Finally, we propose an architecture that uses IADev with the Siemens IoT cloud platform for service orchestration in industrial IoT.

Application of machine learning algorithms in lane-changing model for intelligent vehicles exiting to off-ramp

ABSTRACT The primary objective of this study is to evaluate how intelligent vehicles equipped with cooperative adaptive cruise control (CACC) improve freeway efficiency and safety at an off-ramp bottleneck. Applying randomized forest and back-propagation neural network (BPNN) algorithms, lane-changing characteristics are obtained based on ground-truth vehicle trajectory data extracted from the NGSIM dataset. The results show that both CACC penetration rate and length of diverge influence areas exert considerable influence on road capacity and traffic safety. Overall, the capacity will peak after an initial decrease as the CACC penetration rate increases. The maximum capacity obtained in 100% of CACC vehicle scenarios improved by over 60%, compared with 50% CACC penetration rate scenario. The proposed integration system with 100% CACC penetration rate significantly reduced the rear-end collision risks, decreasing time exposed time-to-collision and time integrated time-to-collision by 70.8%–97.5%.

Enabling Efficient Coexistence of DSRC and C-V2X in Vehicular Networks

Radio access technologies, such as C-V2X and DSRC, have been used to support robust communication in connected vehicles scenarios. However, existing studies mostly dealt with homogenous vehicular networks, where coexistence of different radio access technologies in vehicles are not considered. More precisely, such multi-radio interface environments burden communications among vehicles. In this article, we first review DSRC and C-V2X radio access technologies and existing packet relaying mechanisms that are specifically designed for homogenous or heterogeneous vehicular environments. We then present a quality of service aware relaying algorithm (QR) that incorporates multi-metric to prioritize dual interface vehicles (DVs) and provides robust communications among vehicles that are equipped with different radio access technologies. The simulation results confirm the superiority of the proposed QR in terms of message reception and relaying count as compared to the standard protocols.

Calm Commute

Commutes provide an opportune time and space for interventions that mitigate stress-particularly stress accumulated during the workday. In this study, we test the efficacy and safety of haptic guided slow breathing interventions of short duration while driving. We also present design and experimental implications for evolving these interventions from prior simulator to moving vehicle scenarios. We ran a controlled study (N=24) testing a haptic guided breathing system in a closed circuit under normal and stressful driving conditions. Results show the intervention to be successful in both user adoption and system effectiveness with an 82% rate of engagement in intervention and clear reduction of breathing rate and physiological arousal, with no effect on driving safety and minimal effect on performance. The haptic intervention received positive acceptance from the participants: all indicated a willingness to engage with the intervention in the future and all rated the intervention as safe for traffic applications. The results of this study encourage further investigations exploring the use of the intervention on public roads and monitoring for longitudinal health benefits.

Disanalogical Discourse on Trolley Problem for Autonomous Vehicles

Autonomous vehicles promise to be safer than manually driven counterpart. Yet they are still to become completely safe. Collisions are practically unavoidable. So autonomous vehicles need to be algorithmically modeled for how they ought to respond to scenarios where collisions are highly probable or inevitable. The accident-scenarios autonomous vehicles might face have been frequently linked to dilemmas associated with the trolley problem. In this review article, we critically examine this ubiquitous analogy. We observe three basic concerns in which the ethics behind accident algorithms for autonomous vehicles and the philosophy of trolley problem differ: a. The algorithmic design follows a stakeholder model or an agency model. b. Legal framework and moral responsibilities. c. Modelling low-latency decision-making in the face of uncertainty and risk. By reviewing these three areas of dis-analogy, we identify that Trolley Problem is an abstract problem that is of low relevance to the real-life situation of a crash scenario of autonomous vehicle. Every crash scenario is unique to the people it affects, both passenger and pedestrian. Care Ethics seems to be more suitable approach for such phenomenon as its result adapts to context of the real-life.

Pedestrian Detection Based on Two-Stream UDN

Pedestrian detection is the core of the driver assistance system, which collects the road conditions through the radars or cameras on the vehicle, judges whether there is a pedestrian in front of the vehicle, supports decisions such as raising the alarm, automatically slowing down, or emergency stopping to keep pedestrians safe, and improves the security when the vehicle is moving. Suffering from weather, lighting, clothing, large pose variations, and occlusion, the current pedestrian detection still has a certain distance from the practical applications. In recent years, deep networks have shown excellent performance for image detection, recognition, and classification. Some researchers employed deep network for pedestrian detection and achieve great progress, but deep networks need huge computational resources, which make it difficult to put into practical applications. In real scenarios of autonomous vehicles, the computation ability is limited. Thus, the shallow networks such as UDN (Unified Deep Networks) is a better choice, since it performs well while consuming less computation resources. Based on UDN, this paper proposes a new deep network model named two-stream UDN, which augments another branch for solving traditional UDN’s indistinction of the difference between trees/telegraph poles and pedestrians. The new branch accepts the upper third part of the pedestrian image as input, and the partial image has less deformation, stable features, and more distinguished characters from other objects. For the proposed two-stream UDN, multi-input features including the HOG (Histogram of Oriented Gradients) feature, Sobel feature, color feature, and foreground regions extracted by GrabCut segmentation algorithms are fed. Compared with the original input of UDN, the multi-input features are more conducive for pedestrian detection, since the fused HOG features and significant objects are more significant for pedestrian detection. Two-stream UDN is trained through two steps. First, the two sub-networks are trained until converge; then, we fuse results of the two subnets as the final result and feed it back to the two subnets to fine tune network parameters synchronously. To improve the performance, Swish is adopted as the activation function to obtain a faster training speed, and positive samples are mirrored and rotated with small angles to make the positive and negative samples more balanced.

A Realization Method of Video Information Exchange for Network Connected Vehicles

This article through the design which has the function of transcoding, distribution and authentication exchange unit, achieve a face made car video information exchange method. REST method is used to realize the interface call of video information exchange, which includes control flow, media flow and structured data flow of video content analysis results. It can be used in video exchange scenarios of vehicle and vehicle, vehicle and infrastructure, vehicle and remote management platform.

Distribution of Data Dissemination Model using DTN Routing Protocols

In this research paper compare the protocol’s performance together with the experimental results of optimal routing using real-life scenarios of vehicles and pedestrians roaming in a city. In this research paper, conduct several simulation comparison experiments(in the NS2 Software) to show the impact of changing buffer capacity, packet lifetime, packet generation rate, and number of nodes on the performance metrics. This research paper is concluded by providing guidelines to develop an efficient DTN routing protocol. To the best of researcher(Parameswari et al.,) knowledge, this work is the first to provide a detailed performance comparison among the diverse collection of DTN routing protocols.

VANET Simulator: Full Design Architecture

Vehicular ad-hoc networks VANETs are now greatly affecting our daily lives. Autonomous driving vehicles becomes a hot research topic in automotive industry. Deploying autonomous vehicles scenarios requires realistic network through which all vehicles can communicate safely and efficiently. Network simulation prior direct implementation saves a lot of time and money, due to the high implementation cost of the vehicular ad-hoc networks.VANET simulators are not as same as mobile ad-hoc network MANET simulators; they have different characteristics and different architecture design, where VANET simulators are rapidly changing network topology in a frequent basis with high-speed moving nodes.In this paper, we will discuss the full architecture design of VANET simulator, which consists of three main parts: mobility generator, network simulator, and a middleware framework that combine both mobility and network simulator together, followed by a comparative study of the different parts of VANET simulator.

Modeling and understanding the impacts of efficiency measures on fleet fuel consumption in vehicle importing countries: A case study of Qatar

Globally, transportation always rank in the top three emission sectors next to industry and buildings sectors. Thus, any efforts to achieve efficiency would lead to significant reductions in the emissions. Transition to new technology vehicles with clean and alternative fuels is an important way of achieving such emission reductions. Countries that import vehicles rely on the technology improvements and laws of other countries to increase the fuel efficiency of their vehicle stock. This research investigates the impact of such a scenario by considering the case study of Qatar, which is a country that is oil- and gas-rich and imports all of its vehicle stock. The model developed in this study, by the combined use of system dynamics and vehicle cohort modeling, is the first of its type in this region. Moreover, it addresses the research gaps by comparing the market penetration and sales of gasoline, hybrid-electric, and battery electric vehicles. The results reveal that despite alternative vehicles dominating the sales, gasoline vehicles have remained in the stock for more than 10 years after the complete stoppage of their sales. This is because of the system inertia created by the volume of vehicles in stock owing to their long lifetime. Nonetheless, in the extreme scenario of battery electric vehicles dominating the market, hybrid-electric and battery electric vehicles significantly reduce fuel consumption and emissions, with an estimated emission reduction of more than 8 million tons of carbon dioxide. These savings are not at the same level in terms of the fuel because one (gasoline) vehicle is replaced by another one (in which electricity is generated via natural gas), with the electricity consumption increasing by more than 8 TWh.

Towards Rear-End Collision Avoidance: Adaptive Beaconing for Connected Vehicles

Connected vehicles have been considered as an effective solution to enhance driving safety as they can be well aware of nearby environments by exchanging safety beacons periodically. However, under dynamic traffic conditions, especially for dense-vehicle scenarios, the naive beaconing scheme where vehicles broadcast beacons at a fixed rate with a fixed transmission power can cause severe channel congestion and thus degrade the beaconing reliability. In this paper, by considering the kinematic status and beaconing rate together, we study the rear-end collision risk and define a danger coefficient ρ to capture the danger threat of each vehicle being in the rear-end collision. In specific, we propose a fully distributed adaptive beacon control scheme, called ABC, which makes each vehicle actively adopt a minimal but sufficient beaconing rate to avoid the rear-end collision in dense scenarios based on individually estimated ρ. With ABC, vehicles can broadcast at the maximum beaconing rate when the channel medium resource is enough and meanwhile keep identifying whether the channel is congested. Once a congestion event is detected, an NP-hard distributed beacon rate adaptation (DBRA) problem is solved with a greedy heuristic algorithm, in which a vehicle with a higher ρ is assigned with a higher beaconing rate while keeping the total required beaconing demand lower than the channel capacity. We prove the heuristic algorithm's close proximity to the optimal result and thoroughly analyze the communication overhead of ABC scheme. By using Simulation of Urban MObility (SUMO)-generated vehicular traces, we conduct extensive simulations to demonstrate the efficacy of our proposed ABC scheme. Simulation results show that vehicles can adapt beaconing rates according to the driving safety demand, and the beaconing reliability can be guaranteed even under high-dense vehicle scenarios.