Advanced Vehicle Technologies Research Articles

Evaluating the Associations between Forward Collision Warning Severity and Driving Context

Forward collision warning (FCW) systems typically employ forward sensing technologies to identify possible forward collisions and provide an alert to the driver in the event they have not recognized a threat. These systems have demonstrated safety benefits. However, because the base rate of collisions is low, sensitive FCW systems can provide a high rate of alarms in situations with no or low probability of collision, which may negatively impact driver responsiveness and satisfaction. We examined over 2000 naturally occurring FCWs in two modern vehicles as a part of a naturalistic driving study investigation into advanced vehicle technologies. Analysts used cabin and forward camera footage, as well as environmental characteristics, to judge the likelihood of a crash during each alert, which were used to model the likelihood of an alert representing a possible collision. Only nine FCWs were considered “crash possible and imminent”. Road-type, speed, traffic density, and deceleration profiles distinguished between alert severity. Modeling outcomes provide clues for reducing nuisance and false alerts, and the method of using subjective video annotation combined with vehicle kinematics shows promise for investigating FCW alerts in the real world.

Speeding behavior while using adaptive cruise control and lane centering in free flow traffic

Objective Adaptive cruise control (ACC) and lane centering are usually marketed as convenience features but may also serve a safety purpose. However, given that speeding is associated with increased crash risk and worse crash outcomes, the extent to which driver’s speed using ACC may reduce the maximum safety benefit they can obtain from this system. The current study was conducted to characterize speeding behavior among drivers using adaptive cruise control and a similar system with added lane centering. Methods We recruited 40 licensed adult drivers from the Boston, Massachusetts, metro area. These drivers were given either a 2017 Volvo S90 or a 2016 Range Rover Evoque to use for about 4 weeks. Results Drivers were significantly more likely to speed while they used ACC (95%) relative to periods of manual control (77%). A similar pattern arose for drivers using ACC with added lane centering (96% vs. 77%). Drivers who traveled over the posted limit with these systems engaged also sped slightly faster than drivers controlling their vehicle manually. Finally, we found that these differences were the most pronounced on limited-access roads with a lower speed limit (55 mph). Conclusions These findings point to a possible obstacle to obtaining the full safety potential from this advanced vehicle technology. Any consideration of the net safety effect of ACC and lane centering should account for the effects of more frequent and elevated speeding.

First-Stage Evaluation of a Prototype Driver Distraction Human-Machine-Interface Warning System

Recent advances in vehicle technology permit the real-time monitoring of driver state to reduce distraction-related crashes, particularly within the heavy vehicle industry. Relatively little published research has evaluated the human machine interface (HMI) design for these systems. However, the efficacy of in-vehicle technology depends in large part on the acceptability among drivers of the system’s interface. Four variations of the HMI of a prototype multi-modal warning system developed by the authors for driver distraction were evaluated in a truck simulator with eight car drivers and six truck drivers. Driver acceptance of the HMIs was assessed using the System Acceptability Scale; and salience, comprehension and perceived effectiveness of components of the HMIs (modality, intensity of warning) were assessed using likert scales. The results showed that participants considered the HMIs to be acceptable and useful, and that the warning components were largely noticed, understood correctly, and perceived to be effective. Although this study identified no major design flaws with the recently developed HMIs, further simulator testing with a larger sample size is recommended to validate the findings. On-road evaluations to assess the impact of the HMIs on real world safety are a necessary pre-requisite for implementation.

Modelling the adoption of autonomous vehicle: How historical experience inform the future preference

This study investigates individuals’ preference towards autonomous vehicle (AV) as a privately-owned vehicle and as a vehicle in shared services. One of the key features of this study is to examine the effects of historical changes and experiences over the life course towards the adoption of AVs. A joint bivariate ordered probit model is developed using data from a retrospective survey conducted in the Okanagan region of British Columbia, Canada. The model captures unobserved error correlation between the response variables: preference towards AV ownership and using AV as a vehicle in shared services. The model confirms the existence of a significant error correlation between preferences for AV ownership and sharing. The model results suggest that AV adoption is significantly affected by individuals’ historical experiences over the life course, such as exposure to technology, household characteristics, vehicle ownership, and accessibility and built-environment attributes of the residential location. For example, individuals with historical exposure to technologies including average smartphone ownership/person in the household over the life course and availability of advanced vehicle technology such as lane assist, parking assist, and autonomous emergency stop in the current and previously owned vehicles are more likely to adopt AVs. Individuals historically living in suburban areas have a higher likelihood of owning AVs whereas historically urban dwellers living in higher mixed land use neighbourhoods over the lifetime are more likely to prefer AV sharing. Ownership of technology devices such as smartphones and google home may lead to owning as well as sharing AVs. In addition, the model retains the effects of attitudinal factors, and socio-demographics. For instance, pro-environment individuals are likely to own and share AVs. Pro-shared-mobility individuals are more likely to share AVs whereas they are less interested in owning.

Advanced vehicle technology: Mapping mental model accuracy and system exposure to driver behavior

Advanced driver assistance systems have potential to increase safety and comfort for drivers; however, drivers need to understand the capabilities and limitations of these systems to use them appropriately. This study sought to explore how the quality (accuracy) of drivers’ mental models of adaptive cruise control (ACC) impacted their behavior and interactions while using the system. Seventy-eight participants drove in a high-fidelity driving simulator while operating an ACC system, in normal conditions and while interacting with the system interface. Participants with stronger (more accurate) mental models glanced to the road ahead more often during normal conditions early on compared to drivers were less accurate mental models; however, these differences diminished with increased system exposure. Glance behavior while interacting with the system and time to complete the interactions were less effected by the strength of the participant’s mental model. Results are discussed in the context of driver education and training.

Rethinking the carbon dioxide emissions of road sector: Integrating advanced vehicle technologies and construction supply chains mitigation options under decarbonization plans

The single life cycle assessment on advanced vehicle technologies and clean construction supply chains offers the potential for reducing carbon dioxide emissions from vehicle operation and road infrastructure separately. However, the impact of the combination of these two aspects on CO2 emissions mitigation of road life cycle is less known even though it is critical to the longer-term road transportation decarbonization and long-range policy making for transportation sector, especially under latest worldwide net-zero carbon emission goal. In this study, taking a real project-level asphalt pavement freeway with 15-year life span in China as a case, the amount and hot spots of CO2 emissions of road transportation were evaluated by considering emissions abatement measures of advanced vehicle technologies and construction supply chains which are theoretically practicable in near-, mid-term and long-term future (from 2020 to 2035). Furthermore, a scenario-based sensitivity analysis on different increasing scope together with various increasing degree of abatement levels over time for these measures is provided. Results demonstrate that the timely implementation of combined decarbonation measures of advanced vehicle technologies and construction supply chains by 2035 could reduce 16.3% of total CO2 emissions from a road life cycle. In detail, for maintenance activities and use phase, total CO2 emissions reduce 32% and 16.2% separately. The construction equipment still is the top emissions contributor for maintenance activities. The hybrid electric vehicles (HEV), not battery electric vehicles (EV), replaces conventional internal combustion engine vehicles (ICEV) and becomes the largest source for passenger-related CO2 emissions from use phase by 2035. Findings are sensitive to assumptions regarding cleaner fuel-produced cement, construction equipment and asphalt, and the market penetration of new energy vehicle. The tight and accelerating pace of primary abatements assist in alleviating the infrastructure carbon lock-in effects caused by maintenance activities with 15% additional CO2 emissions saving, but not bring about a significant additional reduction (no more than 2.0%) for total road life cycle CO2 emissions. Current abatement measures are effective but obvious emissions mitigation substitutes of a road is still required, especially innovative road structure and cleaner supply chains inputs as well as deeper decarbonized electrified vehicle.

Real-world emission characteristics of black carbon emitted by on-road China IV and China V diesel trucks

Diesel vehicle is an important source of black carbon (BC). A portable emission measurement system including a photo-acoustic extinctiometer and SEMTECH-LDV was used to measure the real-world emissions of 14 light-duty and heavy-duty diesel trucks (LDDTs and HDDTs, meeting the China IV and China V standards) in Beijing. BC emission factors and the BC/PM2.5 ratio were obtained, and the effects of the vehicle type, emission standard and driving cycle on emissions were analyzed. The tightening of emission standards and the advancement of vehicle technology have reduced BC emissions from the China II standard to the China V standard. The emission reductions of BC are lower than those of other components of PM2.5 from the China II standard to the China IV standard but higher from the China IV standard to the China V standard. The BC and PM2.5 had the same main sources for the HDDTs and China IV LDDTs but had different sources for the China V LDDTs having diesel particulate filters. The BC/PM2.5 ratios of LDDTs, and HDDTs decreased from the China IV standard to the China V standard by 97.2% and 38.2%, respectively. The BC/PM2.5 ratio for China V LDDTs was 10 to 20 times lower than that for other diesel vehicles. The BC emissions tested under the highway driving cycle were 39.4% ± 16.7% lower than those under the no-highway driving cycle, but the BC/PM2.5 ratios had the opposite tendency. More China V and China VI heavy-duty diesel vehicles equipped with diesel particulate filters need to be tested to obtain more accurate BC/PM2.5 data and to improve the readiness of emission inventory calculations. The findings of this study help clarify the BC emission characteristics of diesel vehicles on actual roads and provide scientific basis for the formulation of emission control strategies for diesel vehicles in China.

Older adults and their willingness to use semi and fully autonomous vehicles: A structural equation analysis

Advanced Vehicle Technologies (AVTs) have been purported to improve the behind-the-wheel experience of older adults (65+ years) by making driving safer, easier, and more enjoyable. The autonomous vehicle, in particular, is viewed as a promising innovation for those in older age groups. However, little is known about how older adults perceive AVTs as well as their attitudes, concerns, and willingness to use different levels of vehicular automation.Using the results of an online survey of 1000 Canadians aged 65 and older (who live in the Greater Golden Horseshoe Region, Southern Ontario, Canada), this paper aims to identify and quantify key factors that can affect older adults' willingness to use vehicles with different levels of automation (e.g., semi vs. fully autonomous vehicles; SAV/FAV). Underlying this objective is a need to examine older adults' preferences and concerns for using emerging automotive technologies.Using factor analysis and structural equation modeling, several factors were also identified that can influence older adults' openness to use SAV/FAV including the perception that the AVTs would actually assist with performing driving-related tasks, participant concerns about using SAV/FAV (mainly concerns about inadequate testing on SAV/FAV and being legally responsible if SAV/FAV technology failure causes an accident), using automobile as a passenger, gender, level of education, and the need for assistance with driving. Moreover, almost two thirds of participants reported having concerns about liability if an AVT failed resulting in a crash.Findings of this study provide decision makers and car manufacturing companies with valuable insights regarding older adults' willingness and needs to use SAV/FAV.

Identification of Priority Areas of Improvement for Small Passenger Car Segment in Indian Market

The present study reports an investigation on identification of priority areas of improvement for small passenger car segment in the Indian market. A total of 750 responses on importance and satisfaction rating of 16 attributes in a 7-point Likert-type ordinal rating scale were collected from small car owners in Delhi using a paper and pencil instrument. Revised importance-performance analysis with fuzzy c-means clustering algorithm was used to identify the priority areas of improvement in small passenger cars. The analysis technique effectively identified the factor structure of consumer satisfaction and derived various management schemes for small passenger cars by analysing consumers’ importance and satisfaction data. The priority areas of improvement were obtained by comparing the factor structure with the management scheme. The results indicate that safety, security and advanced vehicle technology options are the priority areas of improvement for small passenger cars. The study identified that the consumers are satisfied with the performance of purchase price, driving range, annual fuel cost, annual maintenance cost, top speed, seating comfort, appearance/style, emission, acceleration time and resale value in small cars, indicating the need to retain these attributes at their present levels to maintain competitive advantage. The engine power is identified under ‘possible overkill’, which indicates an opportunity for automobile manufacturers to possibly reduce the engine power to save some resources and reallocate the same for improvement of priority attributes. The findings of the present study provide valuable insight into the product specifications that the automobile manufacturers should focus on, to make new-generation small cars more attractive to Indian consumers.

Blueprint for a Simulation Framework to Increase Driver Training Safety in North America: Case Study

Despite numerous recent advances in the classroom and in-vehicle driver training and education over the last quarter-century, traffic accidents remain a leading cause of mortality for young adults—particularly, those between the ages of 16 and 19. Obviously, despite recent advances in conventional driver training (e.g., classroom, in-vehicle, Graduated Driver Licensing programs), this remains a critical public safety and public health concern. As advanced vehicle technologies continue to evolve, so too does the unintended potential for mechanical, visual, and/or cognitive driver distraction and adverse safety events on national highways. For these reasons, a physics-based modeling and high-fidelity simulation have great potential to serve as a critical supplementary component of a near-future teen-driver training framework. Here, a case study is presented that examines the specification, development, and deployment of a “blueprint” for a simulation framework intended to increase driver training safety in North America. A multi-measure assessment of simulated driver performance was developed and instituted, including quantitative (e.g., simulator-measured), qualitative (e.g., evaluator-observed), and self-report metrics. Preliminary findings are presented, along with a summary of novel contributions through the deployment of the training framework, as well as planned improvements and suggestions for future directions.

Are limitations of advanced vehicle technologies described consistently for different vehicle models: An examination for Adaptive Cruise Control

Vehicle manufacturers are increasingly offering increasingly sophisticated types of Advanced Driver Assistance Systems (ADS) in late model year vehicles, not just in higher end models but even in e...

Advanced vehicle technologies and road safety: A scoping review of the evidence

The proliferation of Advanced Vehicle Technologies (AVTs) has generated both excitement and concern among researchers, policymakers, and the general public. An increasing number of driver assistance systems are already available in today’s automobiles; many of which are expected to become standard. Therefore, synthesizing the available evidence specific to the safety of AVTs is critical. The goal of this scoping review was to summarize this evidence with a focus on AVTs that require some driver oversight (i.e., Levels 0–3 as per the Society of Automotive Engineers (SAE) levels of automation taxonomy).A scoping review of research literature on AVTs was conducted for studies up to March 2018. Inclusion criteria consisted of: any study with empirical data of AVTs that included male and female drivers aged 16 years and older, healthy people (i.e., without impairments), passenger vehicles, driving simulators and/or large databases with road safety information that could be analyzed for the purpose of examining AVTs (SAE Levels 0–3), as well as measures of driving outcomes.A total of 324 peer-reviewed studies from 25 countries met the inclusion criteria for this review with over half published in the last 5 years. Data was extracted and summarized according to the following categories: measures used to evaluate the effect of AVTs on road safety (objective) and driver perceptions of the technology (subjective), testing environment, and study populations (i.e., driver age). The most commonly reported objective measures were longitudinal control (50 %), reaction time (40 %), and lateral position (23 %). The most common subjective measures were perceptions of trust (27 %), workload (20 %), and satisfaction (17 %). While most studies investigated singular AVTs (237 of 324 studies), the number of studies after 2013 that examined 2 or more AVTs concurrently increased. Studies involved drivers from different age groups (51 %) and were conducted in driving simulators (70 %). Overall, the evidence is generally in favour of AVTs having a positive effect on driving safety, although the nature and design of studies varied widely.Our examination of this evidence highlights the opportunities as well as the challenges involved with investigating AVTs. Ensuring such technologies are congruent with the needs of drivers, particularly younger and older driver age groups, who are known to have a higher crash risk, is critical. With automotive manufacturers keen to adopt the latest AVTs, this scoping review highlights how testing of this technology has been undertaken, with a focus on how new research can be conducted to improve road safety now and in the future.

Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis.

Advances in vehicle technology have resulted in the development of vehicles equipped with sensors to acquire standardized information such as engine speed and vehicle speed from the in-vehicle controller area network (CAN) system. However, there are challenges in acquiring proprietary information from CAN frames, such as the brake pedal and steering wheel operation, which are essential for driver behavior analysis. Such information extraction requires electronic control unit identifier analysis and accompanying data interpretation. In this paper, we present a system for the automatic extraction of proprietary in-vehicle information using sensor data correlated with the desired information. First, the proposed system estimates the vehicle’s driving status through threshold-, random forest-, and long short-term memory-based techniques using inertial measurement unit and global positioning system values. Then, the system segments in-vehicle CAN frames using the estimation and evaluates each segment with our scoring method to select suitable candidates by examining the similarity between each candidate and its estimation through the suggested distance matching technique. We conduct comprehensive experiments of the proposed system using real vehicles in an urban environment. Performance evaluation shows that the estimation accuracy of the driving condition is 84.20%, and the extraction accuracy of the in-vehicle information is 82.31%, which implies that the presented approaches are quite feasible for automatic extraction of proprietary in-vehicle information.

Proposed Framework for Identifying and Predicting Operator Errors When using Advanced Vehicle Technologies

Advanced vehicle technologies include systems that are defined by the Society for Automotive Engineers as automated driving features or driver support features. The latter are increasingly available in late model vehicles in the form of advanced driver assistance systems (ADAS). ADAS features remove some responsibilities from drivers, but still depend on the drivers for safe operation. This can result in drivers committing errors while using ADAS, especially if their understanding of these systems, that is, their mental model, is incorrect. To understand how these systems could be used incorrectly it is necessary to have an insight into these mental models. One approach is to characterize users’ mental representations of systems based on the errors that they commit during system use. Such an approach necessitates a classification of potential errors that may be committed, and the underlying cognitive and behavioral reasons for such errors. To that end, a framework is proposed that can, among other goals, help predict user errors while using ADAS based on human factors and task analysis techniques. A methodology is detailed for mapping operator-system interactions using state diagrams, error identification techniques using task analysis are proposed, and a categorization scheme based on classic error taxonomies is described. This proposed framework can subsequently be expanded for error identification for a wider range and versions of ADAS, as well as for future automated driving systems (ADS). Moreover, the framework provides a systematic approach that can be used toward operationalizing mental models, forming the basis for structured user training, and for human-centered design of advanced vehicle technologies.

What are the best combinations of fuel-vehicle technologies to mitigate climate change and air pollution effects across the United States?

The transportation sector is the largest contributor to CO2 emissions and a major source of criteria air pollutants in the United States. The impact of climate change and that of air pollution differ in space and time, but spatially-explicit, systematic evaluations of the effectiveness of alternative fuels and advanced vehicle technologies in mitigating both climate change and air pollution are lacking. In this work, we estimate the life cycle monetized damages due to greenhouse gas emissions and criteria air pollutant emissions for different types of passenger-moving vehicles in the United States. We find substantial spatial variability in the monetized damages for all fuel-vehicle technologies studied. None of the fuel-vehicle technologies leads simultaneously to the lowest climate change damages and the lowest air pollution damages across all U.S. counties. Instead, the fuel-vehicle technology that best mitigates climate change in one region is different from that for the best air quality (i.e. the trade-off between decarbonization and air pollution mitigation). For example, for the state of Pennsylvania, battery-electric cars lead to the lowest population-weighted-average climate change damages (a climate change damage of 0.87 cent/mile and an air pollution damage of 1.71 cent/mile). In contrast, gasoline hybrid-electric cars lead to the lowest population-weighted-average air pollution damages (a climate change damage of 0.92 cent/mile and an air pollution damage of 0.77 cent/mile). Vehicle electrification has great potential to reduce climate change damages but may increase air pollution damages substantially in regions with high shares of coal-fired power plants compared to conventional vehicles. However, clean electricity grid could help battery electric vehicles to achieve low damages in both climate change and air pollution.

Age-Related Differences in the Cognitive, Visual, and Temporal Demands of In-Vehicle Information Systems.

In-vehicle information systems (IVIS) refer to a collection of features in vehicles that allow motorists to complete tasks (often unrelated to driving) while operating the vehicle. These systems may interfere, to a greater extent, with older drivers’ ability to attend to the visual and cognitive demands of the driving environment. The current study sought to examine age-related differences in the visual, cognitive and temporal demands associated with IVIS interactions. Older and younger drivers completed a set of common tasks using the IVIS of a representative sample of six different vehicles while they drove along a low-density residential street. Evaluation measures included a Detection Response Task (DRT), to assess both cognitive and visual attention, and subjective measures following each condition using the NASA Task Load Index (TLX). Two age cohorts were evaluated: younger drivers between 21 and 36 years of age, and older drivers between 55 and 75 years of age. Participants completed experimental tasks involving interactions with the IVIS to achieve a specific goal (i.e., using the touch screen to tune the radio to a station; using voice commands to find a specified navigation destination, etc.). Performance of tasks varied according to different modes of interaction available in the vehicles. Older drivers took longer to complete tasks, were slower to react to stimuli, and reported higher task demand when interacting with IVIS. Older drivers stand to benefit the most from advancements in-vehicle technology, but ironically may struggle the most to use them. The results document significant age-related costs in the potential for distraction from IVIS interactions on the road.

The Impact of Driver’s Mental Models of Advanced Vehicle Technologies on Safety and Performance

The Impact of Driver’s Mental Models of Advanced Vehicle Technologies on Safety and Performance

Preface to the Special Issue on Human Factors and Advanced Vehicle Automation: Of Benefits, Barriers, and Bridges to Safe and Effective Implementation.

The aim of this special issue is to bring together the latest research related to driver interaction with various types of vehicle automation. Vehicle technology has undergone significant progress over the past decade, bringing new support features that can assist the driver and take on more and more of the driving responsibilities. This issue is comprised of eight articles from international research teams, focusing on different types of automation and different user populations, including driver support features through to highly automated driving systems. The papers comprising this special issue are clustered into three categories: (a) experimental studies of driver interactions with advanced vehicle technologies; (b) analysis of existing data sources; and (c) emerging human factors issues. Studies of currently available and pending systems highlight some of the human factors challenges associated with the driver-system interaction that are likely to become more prominent in the near future. Moreover, studies of more nascent concepts (i.e., those that are still a long way from production vehicles) underscore many attitudes, perceptions, and concerns that will need to be considered as these technologies progress. Collectively, the papers comprising this special issue help fill some gaps in our knowledge. More importantly, they continue to help us identify and articulate some of the important and potential human factors barriers, design considerations, and research needs as these technologies become more ubiquitous.

A spatial analysis of the ownership of alternative fuel and hybrid vehicles

Understanding the ownership of alternative fuel vehicles (AFVs) and advanced powertrain vehicle technologies is critical for transitioning to low Carbon. This study explores the choices made by early adopters of AFVs. To identify the ownership behavior of early adopters and their attributes, this study uses the 2017 National Household Travel Survey (NHTS) data. This study contributes by providing new evidence about spatial heterogeneity in consumer preferences for AFVs in space using the most recent travel behavior data. The Geographically Weighted Regression (GWR) is used to explore the spatially varying correlates of AFV ownership. Results reveal significant spatial variations in the associations of AFV ownership and key factors across the United States. For example, high-income households in Southeastern or Northwestern states are more likely to own an AFV. Seniors are with a greater likelihood of owning an AFV relative to young adults, especially in Northeastern and Northwestern states. The effectiveness of incentives is uneven across different states. The incentives are more effective in Florida than in other parts of the country. The number of alternative fuel stations is positively related to AFV ownership. Other spatially varying correlates are related to household size, education, daily trip distance, and online shopping behavior. The spatial modeling applied in this study is a rigorous analytic technique that researchers and practitioners can use to identify local correlates of AFV ownership. The intended outcome of the study is to provide information about consumer preferences regarding owning AFVs, which can potentially impact our transition to a low Carbon economy.

Securing Platinum-Group Metals for Transport Low-Carbon Transition

Summary Fuel cell technology is the key to transport low-carbon transition. It depends on the catalysis from platinum-group metals, the rarest metals on Earth. However, due to the limited reserve, it is critical to evaluate whether global platinum resources can meet the increasing demand. This paper aims to account whether current platinum resources can support future fuel cell vehicle deployment for 140 countries for the period 2000–2100 by establishing a technology-rich, bottom-up model. It is found that, under a wide range of scenarios, the primary resource demand is significantly lower than the supply capacity at the global level. This result confirms that fuel cell vehicles have the potential to significantly contribute to global decarbonization strategies. However, challenges exist at regional and national levels. Most regions will rely heavily on resource import from the few countries with substantial resource endowments but with political instability, implying potential supply risks.