Autonomous vehicles are believed to be the next-generation technology for future societies. The energy and environmental impacts of autonomous vehicles have been realized to be important issues, but ...quantitative analysis is lacking. In this study, by using China's passenger vehicle fleet as an example, we evaluate the effects of autonomous vehicle deployment on greenhouse gas emissions in different scenarios of autonomous vehicle penetration rates and fuel consumption changes. A comprehensive literature review is conducted to support the study. Autonomous vehicles are found to potentially affect the total greenhouse gas emissions in multiple ways, including reducing vehicle ownership, increasing vehicle use intensity, and changing the vehicle fuel consumption rate. These impacts are mostly internally offset such that the overall impact of autonomous vehicle deployment on greenhouse gas emissions is not significant in the near-to mid-term. With a higher autonomous vehicle penetration rate achieved, in the optimistic scenario, a net reduction in greenhouse gas emissions is expected to be realized. In addition, the fuel economy levels of autonomous vehicles are highly uncertain and cause major uncertainties in the simulation results. More field tests and evidence are needed to improve the evaluation reliability.
•The impacts of autonomous vehicles deployment on greenhouse gas are evaluated.•The greenhouse gas emissions impact is not significant in near to mid-term.•A comprehensive literature review is conducted to support the study.•Fuel economy of autonomous vehicles brings the major uncertainty.
With the rising concern about climate change, there has been an increased public awareness that has resulted in new government policies to support scientific research for mitigating these problems. ...Malaysia is among the major energy-intense countries and is under an excessive burden to advance its energy efficiency and to also work towards the reduction of its carbon emission. Plug-in hybrid electric vehicles (PHEVs) have the potential to lessen the carbon emission and gasoline consumption in order to alleviate environmental problems. Most of the energy problems linked to the increasing transportation pollution are now being reduced with the solution of the adoption of PHEVs. PHEVs are seen as a solution to cut carbon emission, which prevents environmental damages. Furthermore, PHEVs’ driving range and performance can be comparable to the other hybrid vehicles as well as the conventional IC engines that have gasoline and diesel tanks. Thus, many efforts are being initiated to promote the use of PHEVs as an innovative and affordable transportation system. In order to achieve making the consumers aware of the adoption of PHEVs, we used a model which is based on the extended theory of planned behavior (TPB). This review is based on the factors affecting the adoption of PHEVs among Malaysian consumers. The model takes into account the ten key features that influence the adoption of PHEVs, such as environmental concern, personal norm, attitude, vehicle ownership costs, driving range, charging time, intention, subjective norm, perceived behavioral control, and personal norm. All these constructs are drivers towards the adoption of PHEVs. These factors affect the relationship between the adoption of PHEVs and how consumers intend to protect the environment. This review is based on improving how the “attitude-action” gap is understood as it is an important element for further studies on PHEVs. The aim of the research is to come up with a framework that examines how to modify the consumer’s environmental concerns in acquiring PHEVs. This will pave the way for more academic research and future works that can emphasize how to obtain empirical results. The authors’ recommendation is that, before a consumer’s behavior is assessed and considered, an observation of the current technology is needed with methods and knowledge of the existing technology adoption aspect.
Energy-management strategy plays a critical role in high fuel economy that modern hybrid electric vehicles can achieve, yet a lack of information about future driving conditions is one of the ...limitations of fulfilling the maximum fuel economy potential of hybrid vehicles. Today, with wider deployment of vehicle telematic technologies, prediction of future driving conditions, e.g., road grade, is becoming more realistic. This paper evaluates the potential gain in fuel economy if road grade information is integrated into the energy management of hybrid vehicles. Real-world road geometry information is utilized in power-management decisions by using both dynamic programming (DP) and a standard equivalent consumption minimization strategy (ECMS). At the same time, two baseline control strategies with no future information are developed and validated for comparison purposes. Simulation results show that road terrain preview enables fuel savings. The level of improvement depends on the cruising speed, control strategy, road profile, and the size of the battery.
This paper first presents an optimization model to flexibly control available plug-in electric vehicle (PEV) battery charging/discharging power based on three-phase power flow and sensitivity ...approaches. This model can achieve one of the two goals: 1) minimizing both battery charging/discharging cost and extra battery degradation cost due to vehicle-to-grid (V2G) activities (cost-reduction strategy); 2) maximizing local peak load shifting and minimizing extra battery degradation cost due to V2G activities (peak-shifting strategy). The first strategy can determine the appropriate charging/discharging rates of an available PEV battery in order to benefit both the PEV owners and the distribution utilities for the day ahead. The second strategy can reduce the peak loads of the system. Both strategies can improve the power quality at the same time. With the help of a sensitivity method, most of the nonlinear constraints are transformed into linear constraints, and the number of constraints is reduced in the model. An interior point optimization approach is utilized to solve the optimization model. The optimization model is modified further to address unexpected PEV connections and travel scenarios during operation. The effectiveness and accuracy of the proposed method are demonstrated and verified on a 75-node test feeder.
Autonomous underwater vehicles (AUVs) are submersible underwater vehicles controlled by onboard computers. AUV formation is a cooperative control which focuses on controlling multiple AUVs to move in ...a group while executing tasks. In contrast to a single AUV, multi-AUV formation represents higher efficiency and better stability for many applications, such as oil and gas industries, hydrographic surveys, and military missions, etc. To achieve better formation, there are several key factors, including AUV performance, formation control, and communication capability. However, most studies in the field of AUV formation mainly focus on formation control methods. We observe that the research of communication capability and AUV performance of multiple AUV formation is still in an early stage. It is beneficial to researchers to present a comprehensive survey of the state of the art of AUV formation research and development. In this paper, we study AUV, formation control, and underwater acoustic communication capability in detail. We propose a classification framework with three dimensions, including AUV performance, formation control, and communication capability. This framework provides a comprehensive classification method for future AUV formation research. It also can be used to compare different methods and help engineers choose suitable formation methods for various applications. Moreover, our survey discusses formation architecture with communication constraints and we identify some common misconceptions and questionable research for formation control related to communication.
Modern vehicles, including connected vehicles and autonomous vehicles, nowadays involve many electronic control units connected through intravehicle networks (IVNs) to implement various ...functionalities and perform actions. Modern vehicles are also connected to external networks through vehicle-to-everything technologies, enabling their communications with other vehicles, infrastructures, and smart devices. However, the improving functionality and connectivity of modern vehicles also increase their vulnerabilities to cyber-attacks targeting both intravehicle and external networks due to the large attack surfaces. To secure vehicular networks, many researchers have focused on developing intrusion detection systems (IDSs) that capitalize on machine learning methods to detect malicious cyber-attacks. In this article, the vulnerabilities of intravehicle and external networks are discussed, and a multitiered hybrid IDS that incorporates a signature-based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks. Experimental results illustrate that the proposed system can detect various types of known attacks with 99.99% accuracy on the CAN-intrusion-dataset representing the IVN data and 99.88% accuracy on the CICIDS2017 data set illustrating the external vehicular network data. For the zero-day attack detection, the proposed system achieves high F1-scores of 0.963 and 0.800 on the above two data sets, respectively. The average processing time of each data packet on a vehicle-level machine is less than 0.6 ms, which shows the feasibility of implementing the proposed system in real-time vehicle systems. This emphasizes the effectiveness and efficiency of the proposed IDS.
The number of pedestrian casualties in crashes with motorised vehicles is still alarming. Misunderstandings about the other road users’ intentions are certainly one contributory factor. Especially ...given recent developments in vehicle automation, informing about ‘vehicle behaviour’ and ‘vehicle intentions’ in the absence of any direct interaction between the driver and the outside world is becoming increasingly relevant. A frontal brake light which communicates that a vehicle is decelerating could be a simple approach to support pedestrians and other road users in the interaction with (potentially automated) motorised vehicles. To assess the effect of a frontal brake light on the identification of vehicle deceleration, the authors conducted a video based lab experiment. The brake light facilitated the identification of decelerations considerably. At the same time, the fact that only half of the decelerations were accompanied by the brake light resulted in increased identification times for decelerations in which the frontal brake light was absent compared to a control condition in which none of the decelerations was indicated by such a light. This finding points towards an increasingly conservative approach in the participants’ assessment of deceleration, which could be interpreted as an indicator of a potential safety effect of the frontal brake light.
Plug-in hybrid electric vehicles (PHEVs) have been regarded as one of several promising countermeasures to transportation-related energy use and air quality issues. Compared with conventional hybrid ...electric vehicles, developing an energy management system (EMS) for PHEVs is more challenging due to their more complex powertrain. In this paper, we propose a generic framework of online EMS for PHEVs that is based on an evolutionary algorithm. It includes several control strategies for managing battery state-of-charge (SOC). Extensive simulation testing and evaluation using real-world traffic data indicates that the different SOC control strategies of the proposed online EMS all outperform the conventional control strategy. Out of all the SOC control strategies, the self-adaptive one is the most adaptive to real-time traffic conditions and the most robust to the uncertainties in recharging opportunity. A comparison to the existing models also employing short-term prediction shows that the proposed model can achieve the best fuel economy improvement but requiring less trip information.
Road transportation is among the global grand challenges affecting human lives, health, society, and economy, caused due to road accidents, traffic congestion, and other transportation deficiencies. ...Autonomous vehicles (AVs) are set to address major transportation challenges including safety, efficiency, reliability, sustainability, and personalization. The foremost challenge for AVs is to perceive their environments in real-time with the highest possible certainty. Relatedly, connected vehicles (CVs) have been another major driver of innovation in transportation. In this paper, we bring autonomous and connected vehicles together and propose TAAWUN, a novel approach based on the fusion of data from multiple vehicles. The aim herein is to share the information between multiple vehicles about their environments, enhance the information available to the vehicles, and make better decisions regarding the perception of their environments. TAWUN shares, among the vehicles, visual data acquired from cameras installed on individual vehicles, as well as the perceived information about the driving environments. The environment is perceived using deep learning, random forest (RF), and C5.0 classifiers. A key aspect of the TAAWUN approach is that it uses problem specific feature sets to enhance the prediction accuracy in challenging environments such as problematic shadows, extreme sunlight, and mirage. TAAWUN has been evaluated using multiple metrics, accuracy, sensitivity, specificity, and area-under-the-curve (AUC). It performs consistently better than the base schemes. Directions for future work to extend the tool are provided. This is the first work where visual information and decision fusion are used in CAVs to enhance environment perception for autonomous driving.
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