Advanced Vehicle Technologies Research Articles

Smart Transportation for All? A Typology of Recent U.S. Smart Transportation Projects in Midsized Cities

Greater integration of advanced vehicle technologies is commonly discussed as a component of developing smart cities, potentially leading to a host of benefits. Final impacts of such benefits are uncertain, though, given research that illustrates induced travel by initial adopters of emerging vehicle technologies and services and mixed effects in transit use and active transportation. The locations within cities where interventions of advanced vehicle technologies are envisioned, geographic scope and extent of integration, and the characteristics of these areas are all likely to influence these effects, and these relationships have received limited investigative attention. To address this, we conducted a comprehensive review of proposals submitted by 78 midsized cities in the United States to create a typology that considers (1) the geographic scope of intervention and (2) the degree of integration of connected and automated vehicles, generating five distinct types of projects. Characteristics of the areas within cities identified for intervention are compared to those of their U.S. Metropolitan Statistical Area (MSA). We identified indicators of comprehensive planning efforts as they relate to sustainability and resilience outcomes in each city. Results show that areas identified by cities for advanced vehicle technology interventions differ in important ways from each city’s broader population that warrant attention relative to known demographic characteristics and behavior of early adopters of transportation technologies. There is also variation in project motivation and municipal planning indicators across typology classifications. These are essential considerations as smart city–aligned transportation interventions continue to develop. Key Words: automated vehicle, connected vehicle, smart city, typology.

A Deep Learning Approach for Vehicle and Driver Detection on Highway

The technology of the detection for vehicle and driver is a popular spot in these ten years. In particular, the driver detection is still a troubled question in the study of public security. In our paper, an algorithm based on YOLOv3 and support vector machine (SVM) is proposed for realizing the detection of vehicles on highway, as well as the detection and binary classification of people in the vehicles, so as to achieve the purpose of distinguishing drivers and passengers and form a one-to-one correspondence between vehicles and drivers. The effectiveness of the algorithm is verified under various complicated highway conditions. Compared with other advanced vehicle and driver detection technologies, the model has a good performance and is robust to road blocking, different attitudes and extreme lighting.

The Importance of Advanced Information Technology and Green Vehicles in Supply Chain Management

This research explores the impact of advanced technology and green vehicles on supply chain performance in the perspective of Mexican manufacturing firms. The study used 153 firms’ data to test hypothesis. The study adopted simultaneous regression statistical method, while the findings show that advanced information technology plays an important role in supply chain improvement through greater-level of information sharing with supply chain partners, reducing the discrepancies and errors on different levels such as forecasting and scheduling. On the other hand, green transportation and vehicles reduce the supply chain cost and improve environmental performance with building strong competitive advantage through more usage of renewable energy, greater level of customer satisfaction and trust, positive image and reputation.

The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self-Driving Cars.

Self-driving vehicles will affect the future of transportation, but factors that underlie perception and acceptance of self-driving cars are yet unclear. Research on feelings as information and the affect heuristic has suggested that feelings are an important source of information, especially in situations of complexity and uncertainty. In this study (N = 1,484), we investigated how feelings related to traditional driving affect risk perception, benefit perception, and trust related to self-driving cars as well as people's acceptance of the technology. Due to limited experiences with and knowledge of self-driving cars, we expected that feelings related to a similar experience, namely, driving regular cars, would influence judgments of self-driving cars. Our results support this assumption. While positive feelings of enjoyment predicted higher benefit perception and trust, negative affect predicted higher risk and higher benefit perception of self-driving cars. Feelings of control were inversely related to risk and benefit perception, which is in line with research on the affect heuristic. Furthermore, negative affect was an important source of information for judgments of use and acceptance. Interest in using a self-driving car was also predicted by lower risk perception, higher benefit perception, and higher levels of trust in the technology. Although people's individual experiences with advanced vehicle technologies and knowledge were associated with perceptions and acceptance, many simply have never been exposed to the technology and know little about it. In the absence of this experience or knowledge, all that is left is the knowledge, experience, and feelings they have related to regular driving.

Emerging Mobility Systems [Guest Editorial

The articles in this special section focus on emerging and new mobile systems with capacities of big data processing and analytics, advanced vehicle technologies, and novel service paradigms are transforming our urban environment and life style. These innovations and breakthroughs have motivated researchers on Intelligent Transportation Systems (ITS) to expand the frontiers of ITS concepts and skillsets, to identify new opportunities and challenges in integrating emerging vehicle technologies and mobility services, and to produce theories, models, algorithms, field experiments and real world applications for establishing a unique ITS discipline. To this end, this special issue is focused on cutting edge ideas, knowledge, techniques, tools and applications in the broad areas on emerging mobility systems.

Perceived safety benefits, concerns, and utility of advanced driver assistance systems among owners of ADAS-equipped vehicles

Objective: There are many unknowns regarding drivers’ use and acceptance of advanced vehicle technologies. This research aimed to examine drivers’ perceptions of advanced driver assistance systems (ADAS).Methods: This research was conducted using structured interviews and focus groups of owners of vehicles with advanced technologies.Results: Drivers’ perceptions about ADAS were mixed, but generally safety was considered to be the greatest value of the systems. There was recognition that the systems may result in overreliance and thus encourage distraction behaviors or other bad driving habits, and participants generally expressed that they were ultimately responsible for the vehicle’s operation and needed to be ready to override the system if it failed.Conclusions: The findings indicate that driver characteristics and individual factors may influence perceptions, behaviors, and interactions with safety technology, and this research is a first step toward understanding any influences. Human factors issues related to automated vehicle technologies are critical for design and deployment, including those of trust, acceptance, and understanding of systems.

Bottleneck mitigation through a variable speed limit system using connected vehicles

ABSTRACTVariable speed limit (VSL) systems are used to improve the traffic conditions by adjusting the speed limits based on the current traffic situation. Advances in vehicle technology have made it possible to use connected vehicles in VSL systems. Connected vehicles can continuously transmit information about their speed and location, which can be used to estimate the current traffic conditions at arbitrary locations. In this study, we propose a VSL system based on connected vehicles. The aim is to also allow application of VSLs for non-recurrent bottleneck mitigation at arbitrary locations, unlike today's systems which require densely placed detectors or are limited to beforehand known bottleneck locations. The proposed system is evaluated by microscopic traffic simulation. The results indicate that the VSL system manage to improve traffic efficiency in a simulated incident scenario.

Direct Government Investment: Perverse Privatization or New Tool of Government?

AbstractIn recent years, public administration scholars have called attention to a blurring of the boundaries between the public and private sectors. However, little attention has been focused on the administration of public programs that seek to impact private markets through direct government investment in private firms. The direct government investment approach is a new tool of government that has been applied in several countries and at multiple levels of government. Through an analytic mix of theory and attention to practice, this article leverages a deep case analysis of the U.S. Department of Energy's Advanced Technology Vehicles Manufacturing Loan Program to propose and utilize criteria for examining justifiable rationales for direct government investment, areas of administrative capacity necessary to manage such investments, and potential pitfalls of this new tool of government.

Modeling Driver Merging Behavior: A Repeated Game Theoretical Approach

Various lane-changing models have been developed for use within microscopic traffic simulation software to replicate driver merging behavior. An understanding of human driving behavior, which can be gained through such modeling, will be critical in harmonizing emerging advanced vehicle technology, such as connected automated vehicles, with human drivers. Therefore, it is important to ensure that lane-changing models are clearly understood, appropriately designed, and carefully calibrated. An earlier study by Kang and Rakha proposed and developed a decision-making model for merging maneuvers using a game theoretical approach considering two drivers: the driver of the subject vehicle (DS) in an acceleration lane and the driver of the following lag vehicle (DL) in the target lane. The previous model assumed that the DS and DL decide on an action at the first point only, where the subject and lag vehicles are identified. The current study extends the Kang and Rakha model by introducing the concept of a repeated game, assuming that a lane change decision is made repeatedly to adjust to changes in surrounding conditions. For example, drivers often decide to change their initial decision as a result of conflicts with other drivers. A repeated game helps the proposed model produce more realistic decision-making in the lane-changing process. To evaluate the model, driver decisions at a certain stage, along with accumulated historical decision data, were extracted from Next Generation SIMulation (NGSIM) data. The validation results reveal that the proposed repeated game model produces considerable prediction accuracy (above 75%).

Design of Low-Cost Vehicle Roll Angle Estimator Based on Kalman Filters and an Iot Architecture.

In recent years, there have been many advances in vehicle technologies based on the efficient use of real-time data provided by embedded sensors. Some of these technologies can help you avoid or reduce the severity of a crash such as the Roll Stability Control (RSC) systems for commercial vehicles. In RSC, several critical variables to consider such as sideslip or roll angle can only be directly measured using expensive equipment. These kind of devices would increase the price of commercial vehicles. Nevertheless, sideslip or roll angle or values can be estimated using MEMS sensors in combination with data fusion algorithms. The objectives stated for this research work consist of integrating roll angle estimators based on Linear and Unscented Kalman filters to evaluate the precision of the results obtained and determining the fulfillment of the hard real-time processing constraints to embed this kind of estimators in IoT architectures based on low-cost equipment able to be deployed in commercial vehicles. An experimental testbed composed of a van with two sets of low-cost kits was set up, the first one including a Raspberry Pi 3 Model B, and the other having an Intel Edison System on Chip. This experimental environment was tested under different conditions for comparison. The results obtained from low-cost experimental kits, based on IoT architectures and including estimators based on Kalman filters, provide accurate roll angle estimation. Also, these results show that the processing time to get the data and execute the estimations based on Kalman Filters fulfill hard real time constraints.

Assessing links between energy consumption, freight transport, and economic growth: evidence from dynamic simultaneous equation models.

We investigate this study to examine the relationship between economic growth, freight transport, and energy consumption for 63 developing countries over the period of 1990-2016. In order to make the panel data analysis more homogeneous, we apply the income level of countries to divide the global panel into three sub-panels, namely, lower-middle income countries (LMIC), upper-middle income countries (UMIC), and high-income countries (HIC). Using the generalized method of moments (GMM), the results prove evidence of bidirectional causal relationship between economic growth and freight transport for all selected panels and between economic growth and energy consumption for the high- and upper-middle income panels. For the lower-middle income panel, the causality is unidirectional running from energy consumption to economic growth. Also, the results indicate that the relationship between freight transport and energy use is bidirectional for the high-income countries and unidirectional from freight transport to energy consumption for the upper-middle and lower-middle income countries. Empirical evidence demonstrates the importance of energy for economic activity and rejects the neo-classical assumption that energy is neutral for growth. An important policy recommendation is that there is need of advancements in vehicle technology which can reduce energy intensity from transport sector and improve the energy efficiency in transport activity which in turn allows a greater positive role of transport in global economic activity.

Examining the role of natural gas and advanced vehicle technologies in mitigating CO2 emissions of heavy-duty trucks: Modeling prototypical British Columbia routes with road grades

This study presents a simulation framework for estimating on-road CO2 emissions of compressed natural gas (CNG) and diesel tractor-trailer heavy-duty trucks under various operational conditions. A second-by-second component-level model was developed and then used to simulate seven distinct drive cycles. This paper specifically considers road grade, and develops a novel technique to pair road grade profiles with given speed vs. time data when gradient data are not available. Six routes around the Canadian province of British Columbia were used as case study drive cycles, including an extreme hill climb route. Results showed that omission of road grade under-estimates CO2 emissions by as much as 24% for both CNG and diesel drivetrains. Simulations indicated that CNG trucks emit 13–15% less CO2 than comparable diesel trucks, depending on weight class and drive cycle. Sensitivity analyses highlighted the importance of aerodynamic drag, rolling friction, and engine efficiency for all cycles. An assessment of advanced vehicle technology options for CNG trucks showed achievable CO2 reductions of 28–35% in the near-term and 41–51% over the longer term, compared to current diesel technology. The same advanced technology options would reduce diesel drivetrain CO2 emissions by 17–23% and 31–42% over the near and long-term respectively. It is worthwhile to emphasize that with commensurate technology developments, CNG drivetrains offer the same 13–15% CO2 reductions compared to diesels over the near and long term. The results demonstrate that CO2 reductions in heavy-duty trucks depend primarily on drivetrain technology, while operational conditions play a less significant role.

An insight of World Health Organization (WHO) accident database by cluster analysis with self-organizing map (SOM)

ABSTRACTObjective: Road crashes are increasing every year in low- and middle-income countries compared to high-income countries, which show decreasing trends. One theory may be because of differences in enforcement of laws, vehicle safety, road standards, and many other factors. A detailed review was made of 5 death trends (total number of deaths/100,000 population and percentages of 4-wheeler, pedestrian, motorized 2/3-wheeler, bicyclist) and the underlying patterns in different countries and regions across the world. This review was done to understand the main reasons for the variances to focus on efficient improvement strategies related to future vehicle and road safety issues.Methods: A self-organizing map (SOM) technique is used to map the nonlinear relationships among different attributes. Overall, 176 countries with 44 attributes were considered from the World Health Organization's (WHO) Global Status Report on Road Traffic Crashes database. Of the 44 attributes, 5 related to accident deaths were considered as response attributes.Results: Very distinct and unique cause–effect patterns for 3 clusters were observed from SOM results. High-income countries were found to have a lower total number of deaths/100,000 population. One theory espouses that this was due to those countries maintaining high vehicle standards and policies, whereas it was quite a different situation for low-income countries. Even though helmet laws were available in Association of South East Asian Nations + 6 (ASEAN + 6) countries, the percentage of 2/3-wheeler deaths may be higher due a lack of enforcement of those laws. Percentage of deaths involving 4-wheeler vehicles was higher in certain countries in the Persian Gulf, the United States, Australia, and New Zealand. This may be due to the fact that these countries have a number of rural areas where drivers drive at highway speeds versus some lower income countries with more urban areas where drivers operate vehicles at slower speeds. Countries with a lack of laws protecting bicyclists saw higher death percentages among bicyclists. The percentage of bicyclist deaths was also higher in areas with no helmet requirement and no investment in infrastructure improvements. The percentage of pedestrian deaths was high when there was no policy to separate road users, especially in low-income African countries. Deaths can be reduced by enforcement of laws and practicing good safety standards related to road traffic.Conclusions: Future vehicle and road safety strategies should consider using advanced statistical tools like SOM to advance safety. Based on a triple-layer (vehicle, infrastructure, and society) safety approach, strict regulations and enforcement are effective measures to reduce fatalities in low- and middle-income countries. On the other hand, introduction of more advanced vehicle technologies will be useful in countries with high gross national incomes (GNIs). Hence, a proper balance of different countermeasures based on economic zones could be effective to reduce total world traffic casualties.

Can Next-Generation Vehicles Sustainably Survive in the Automobile Market? Evidence from Ex-Ante Market Simulation and Segmentation

Introduced autonomous and connected vehicles equipped with emerging technologies are expected to change the automotive market. In this study, using stated preference (SP) data collected from choice experiments conducted in Korea with a mixed multiple discrete-continuous extreme value model (MDCEV), we analyzed how the advent of next-generation of vehicles with advanced vehicle technologies would affect consumer vehicle choices and usage patterns. Additionally, ex-ante market simulations and market segmentation analyses were conducted to provide specific management strategies for next-generation vehicles. The results showed that consumer preference structures of conventional and alternative fuel types primarily differed depending on whether they were drivers or non-drivers. Additionally, although the introduction of electric vehicles to the automobile market is expected to negatively affect the choice probability and mileage of other vehicles, it could have a positive influence on the probability of purchasing an existing conventional vehicle if advanced vehicle technologies are available.

Special Issue on Nanosensing and Microsensing

Sensors, which are transducer-type devices, are indispensable to today’s advanced information society. A huge number of sensors are used not only in everyday devices but also in advanced industrial systems. They are used in Internet of things (IoT) services to gather external information, intelligent robots to recognize the world around them and control their movements, and all advanced vehicle technologies to operate safely and automatically. Sensors detect light, motion, force, fluid flow, electric/magnetic fields, and other physical, chemical, and biological aspects of the external environment. To improve the performances of these sensors, such as their sensitivity, sensing resolution, and power consumption, extensive R&D is conducted in industry and academia. Recent technological progress in MEMS technology has allowed sensors to be manufactured on scales that are increasingly microscopic. More recently, the extreme downsizing of structures to nanometer scale has led to innovative sensing devices called NEMS. This special issue addresses the latest research advances in nanosensing and microsensing science and engineering. It covers a wide range of topics, including novel sensing devices and technologies; small structures fabrication technologies for sensors; MEMS/NEMS sensing devices; physical, chemical, optical and biological sensing devices; and nanoscale science and engineering for sensors. All papers were refereed through careful peer reviews. We would like to express our sincere thanks to the authors for their submissions and to the reviewers for their invaluable efforts. Lastly, we hope this special issue provides valuable and useful information to our interested readers and researchers.

Comparison of fuel efficiency and economical speed for internal combustion engine vehicle and battery electric vehicle using backward-looking simulation

A vehicle system-level model is developed to predict economical speed and fuel efficiency of an Internal combustion engine vehicle (ICEV) and Battery electric vehicle (BEV) based on a backward-looking simulation approach. The modeled vehicles were chosen within the same commercial model line-up, sharing body panels and most interior and exterior components, to allow more relevant comparison between different powertrains. Numerical methods were applied to representative engine fuel consumption and standard motor efficiency maps in the process of grid construction and fuel efficiency calculation. The economical speed of each vehicle was obtained for constant speed based on fuel efficiency. Combined fuel efficiency was also predicted considering a 5-cycle calibration using the federal test procedure (FTP-75) and Highway fuel economy test (HWFET). It is found that the calculated vehicle economical speed differs between powertrain configurations. Model results were validated against the certified fuel economy of each vehicle model as well as with the on road driving test results. Parametric studies were performed for varying coefficients of drag force and rolling resistance, gear shifting criterion, and vehicle test weight to analyze economical speed characteristics and the effect of advanced vehicle technologies on overall fuel efficiency. It is shown that the BEVs were more suitable than ICEVs, particularly in low speed range situations, such as urban driving condition in terms of fuel efficiency, and were also more effective when fuel efficiency improving technologies reducing driving resistances were applied.

Data mining for enhanced driving effectiveness: an eco-driving behaviour analysis model for better driving decisions

With the growing demand for energy efficient vehicles, automobile companies are constantly searching for better ways to study their customers’ driving behaviour for effective new product design and development. One emerging driving behaviour among modern, eco-friendly drivers is the utilising of advanced vehicle technology for smarter, safer and more fuel-efficient driving. While many eco-driving studies focus on minimising fuel consumption, little attention is paid to how the behaviour of an individual driver and the type of vehicle used impact driving effectiveness. This study addresses this gap by proposing a novel overall drive effectiveness index that uses data mining for better driving decisions. Utilising data mining techniques, the index examines the impact of driving behaviour on driving effectiveness. A novel fuel consumption prediction model based on vehicle speed, engine speed and engine load was constructed. This decision-making support model accurately predicts real-time fuel consumption based on different driving behaviours, and hence, the driving effectiveness. Both the proposed index and fuel consumption model can be used to support decision-making in new product design and development.

UPS unveils delivery vehicle with fuel cell range-extender for trial in California

Logistics giant UPS will deploy a prototype electric vehicle featuring a fuel cell range-extender in its Rolling Laboratory fleet of alternative fuel and advanced technology vehicles. UPS is working with the US Department of Energy and other partners to design an innovative zero-emissions, Class 6 medium-duty delivery truck that can match the same route and range requirements as the company's conventional vehicles.

Older People Driving a High-Tech Automobile: Emergent Driving Routines and New Relationships with Driving

Advanced vehicle technologies (AVTs) (e.g., lane departure warning, blind spot monitoring) are sophisticated computer and electronically mediated communications that provide information to users, and, at times, assume control over parts of the driving task (e.g., automated braking). This article examines how AVTs are refashioning older people’s embodied relationships with driving, including driving routines, skills, sensuous dispositions, and modes of control that are considered integral to driving. Results from interviews with 35 older drivers driving a high-tech car call attention to the opportunities and challenges that entanglements with AVTs can present for aging drivers.

Improving stability and curving passing performance for railway vehicles with a variable stiffness MRF rubber joint

With the growing need for effective intercity transport, the need for more advanced rail vehicle technology has never been greater. The conflicting primary longitudinal suspension requirements of high speed stability and curving performance limit the development of rail vehicle technology. This paper presents a novel magnetorheological fluid based joint with variable stiffness characteristics for the purpose of overcoming this parameter conflict. Firstly, the joint design and working principle is developed. Following this, a prototype is tested by MTS to characterize its variable stiffness properties under a range of conditions. Lastly, the performance of the proposed MRF rubber joint with regard to improving train stability and curving performance is numerically evaluated.