Advanced Vehicle Technologies Research Articles

Energy-saving and emission-reduction potential of fuel cell heavy-duty trucks in China during the fuel life cycle.

Exploring alternative fuels and advanced vehicle technology is a crucial strategy for vehicle emission reduction. Fuel cell heavy-duty trucks (FC-HDTs) have a promising application prospect to alleviate the high energy consumption and emissions of road freight, but their environmental performance during the fuel life cycle should be further studied. This study is aimed at evaluating the fossil fuel consumption and GHG emissions of FC-HDTs in China using the updated GREET model. The results show that (1) comparing various hydrogen production pathways, it is found that the coke oven gas (COG) pathway can provide the best environmental performance, while the energy consumption and greenhouse gas (GHG) emissions of the coal gasification (CG) and grid power water electrolysis (GPWE) pathways will be significantly decreased in the future. (2) Among the involved vehicles in China, FC-HDT with GVWR18 has the greatest energy-saving and emission-reduction potential. (3) The application of carbon capture and storage (CCS) technology in hydrogen production is conducive to improving the emission-reduction effect of FC-HDT while increasing its energy consumption slightly. The key to achieving upstream carbon neutrality is to optimize the hydrogen production structure and electricity mix, along with adjusting the hydrogen production process and transportation mode. Furthermore, the fuel economy and payload of the FC-HDT affect its environmental performance, indicating the importance of improving the technology of the drivetrain, fuel cell, and hydrogen storage tank.

High temporal and spatial resolution characteristics of evaporation, transpiration, and evapotranspiration from a subalpine wetland by an advanced UAV technology

Wetland evapotranspiration (ET), which involves the land–atmosphere exchange of energy and water, is dynamic and affects the spatiotemporal distribution of water resources. However, due to the variability and complexity of wetlands, accurate estimation of patch-scale ET and its spatial variability remain insufficiently characterized. To overcome this challenge, an advanced unmanned aerial vehicle (UAV) technology was developed by combining the three-temperature (3T) model, which is robust to estimate transpiration and its spatial variability with UAV-based thermal infrared remote sensing, and Penman equation, which is commonly used to estimate open water evaporation. The combined approach was verified using the Bowen ratio system over a subalpine wetland. The results show that the proposed method is simple and applicable for estimating wetland ET and its spatial variability, with a determination coefficient (R2) of 0.93, mean absolute percentage error (MAPE) of 7.90%, root mean squared error (RMSE) of 0.05 mm h−1, and Nash-Sutcliffe efficiency (NSE) of 0.93. It depicts a large spatial variability in wetland ET with respect to surface vegetation characteristics, water regimes, meteorological factors, and larger transpiration rates than open water evaporation. With its limited inputs and no calibration requirements, the proposed method is concluded to be simple and to easily reveal the high temporal and spatial resolution characteristics of patch-scale ET and its components.

How does training influence use and understanding of advanced vehicle technologies: a simulator evaluation of driver behavior and mental models

Advanced vehicle technologies such as Advanced Driver Assistance Systems (ADAS) promise increased safety and convenience but are also sophisticated and complex. Their presence in vehicles affects how drivers interact with the technologies and how drivers must know about these technologies. To maximize safety benefits, drivers must use such systems appropriately. They must understand how these technologies work and how they may change drivers' traditional responsibilities. Training has been recognized as an effective tool for accelerating knowledge and skills in traditional driving. Consequently, training is gaining recognition as an important tool for improving drivers' knowledge, understanding, and appropriate use of vehicle technologies as well. This study evaluated the effects of different training methods on drivers' use and understanding of vehicle automation, specifically Adaptive Cruise Control (ACC). Licensed drivers with little to no experience with ADAS features were randomly assigned into groups based on three training conditions: two experimental groups, ‘User Manual’ and ‘Visualization’, and a control group with a ‘Sham’ training. Participants were surveyed on their understanding of Adaptive Cruise Control before and after training. They also drove an advanced driving simulator equipped with ACC. The simulated drive offered multiple opportunities for the drivers to interact with the ACC and included embedded cues for engaging with the system and embedded probes to measure driver awareness of the system state. The results found a significant overall increase in knowledge of ACC after training for the experimental groups. Drivers in the experimental training groups also had better real-time awareness of the system state than the control group. The results indicate that training is associated with improved knowledge about the systems. It also shows differential effects of different approaches to training, with text-based training showing greater improvement. These findings have important implications for the design and deployment of these systems, and for policies around driver licensing and education.

A brief review of the rapid transformation of driveline lubricants for hybrid electric and electric vehicles

Hybrid electric and electric vehicles have represented a small portion of the automotive market for many years and mainly use current lubricants, typically automatic transmission fluids (ATFs). However, regulatory compliance to limit greenhouse gases and increased consumer demand have resulted in a rapid global transition to electrified vehicles. This has prompted the need for new advances in vehicle technology to improve efficiency and thereby increase range. Enabling and optimizing such advances requires a new generation of driveline lubricants. Incorporating an electric motor in a transmission or axle, where the motor is exposed to the gear box lubricant, creates new challenges that focus attention on lubricant characteristics that were previously not differentiating features, for example, electrical and thermal properties. Additionally, lubricants must now also be compatible with the constituents used in electric motors which include new polymeric materials and, in some cases, exposed copper. Compatibility tests of these polymers vary within the industry and the risk of copper corrosion in these applications is not always properly assessed by current specification tests. In this paper we will begin with a brief history of electric vehicles, highlight how driveline lubricants, specifically ATFs, have evolved over the years to meet new hardware requirements and then describe the performance requirements expected of lubricants specifically designed for vehicles with electric drive units (EDUs). Our primary goal, however, is to summarize the recent literature that illustrates the changing importance of various lubricant performance properties, new proposed test methods and offer some insight into future e-lubricant evolution.

Does in with the new, mean out with the old? An extended theory of planned behaviour exploring the impact of vehicle technologies on intentions to not wear seatbelts

Approximately one in five Australians killed in motor vehicle crashes will not be wearing a seatbelt. A circulating lay theory suggests that drivers may be questioning the continued necessity of seatbelts given advances in other advanced vehicle technologies. This belief is deeply concerning given the implementation of advanced driver assistance systems in new cars is rapidly increasing. The objective of this research was to apply an extended Theory of Planned Behaviour (TPB), including the additional constructs of trust in vehicle technology and perceived risk of death or injury in the event of a crash, to assess the influence of attitudes, subjective norms, perceived behavioural control (PBC), trust, and perceived risk on drivers’ intentions to engage in seatbelt non-use. The study also aimed to examine if the predictive utility of the extended TPB differed according to the presence of passive (e.g., airbags) active (e.g., lane keeping assist) technologies in vehicles, Further, the influence of underlying behavioural belief, control beliefs, and normative beliefs on driver’s intentions to engage in seatbelt non-use were also explored. Participants (N = 182, 83 males, Mage = 44.23 years) completed a 25-minute online questionnaire. The study was a cross-sectional, between groups design in which all participants were exposed to a baseline control scenario, then randomly allocated to one of four experimental scenarios which manipulated the type of vehicle technologies present in a hypothetical vehicle. Consistent with theoretical expectations of the standard TPB, a hierarchal regression revealed that the constructs of attitude, subjective norms, and PBC (self-efficacy only) significantly and positively predicted seatbelt non-use intentions. Of the extended constructs, trust, but not perceived risk, exhibited a significant effect, and positively predicted seatbelt non-use intentions in the control condition and the active technology condition. Additionally, a critical beliefs analysis found that discomfort was a significant positive predictor of seatbelt non-use intentions. Overall, the findings offer support for the use of the TPB to explain seatbelt non-use intentions and suggest that trust in vehicle technology may influence drivers’ decisions to not wear a seatbelt. This research offers a novel determinant of seatbelt non-use that may be more responsive behavioural change interventions.

A Study on Utilization of High-Ratio Biodiesel and Pure Biodiesel in Advanced Vehicle Technologies

An experimental study was conducted to investigate the effect of high-ratio biodiesel and pure biodiesel on the emissions and performance of Euro4-compliant vehicles. The tested fuels were diesel fuel, biodiesel with a ratio of 30% by volume (B30), biodiesel with a ratio of 50% (B50) and pure biodiesel FAME (B100), while the tested vehicle is of the Euro4-compliant standard currently available in the Indonesian market. In this study, tests on emissions, performance and fuel economy were conducted based on the international standard of the UN ECE R83-05, adopted as UN ECE R-85 and UN ECE R-101 respectively. This study also investigated the effect of the carbon-to-hydrogen ratio on the carbon balance formula. Here, the paper proposed a modified R101 carbon balance formula to calculate the fuel economy for high-ratio and pure biodiesel fuels. The results showed that biodiesel had lower CO, HC and particulate emissions, while NOx emissions were higher compared to diesel fuel. However, pure biodiesel was within the limits imposed by the Euro4 emissions standard. Maximum power output with high-ratio biodiesel decreased by up to 10% with B100. The fuel economy of the B30, B50 and B100 biodiesels was lower than diesel fuel by 3%, 7% and 11%, respectively, based on the modified carbon balance formula for high-ratio biodiesel fuel.

Surrogate Safety Measures: Review and Assessment in Real-World Mixed Traditional and Autonomous Vehicle Platoons

Surrogate safety measures (SSMs) are critical tools for evaluating the safety performance of mixed traffic. Crashes are rare events, and historical crash data is scarce for mixed traffic that includes autonomous and/ or connected vehicles. Recent safety review papers focus on traditional human-driven vehicles (TVs) and do not encompass advanced technology vehicles such as Autonomous Vehicles (AVs), Connected Vehicles (CVs), and Connected-Autonomous Vehicles (CAVs). This study examines the development, implementation, and shortcomings of SSMs, and SSM-based models used for mixed traffic safety evaluation. It reviews the current relevant literature and applies a case study analysis using a real-world mixed traffic dataset. The study summarizes the fundamental SSM guiding concepts, as well as their most significant metrics including threshold values employed in the past for SSMs and SSM-based models. Primary benefits and limitations of examined SSMs and SSM-based models are also underlined. This review reveals significant gaps in the literature that might guide future research paths in SSM-based mixed traffic safety assessment. Critical gaps include the absence of robust SSM threshold selection criteria, the suitability of current SSMs in mixed traffic research, microsimulation modeling that lacks proper calibration and validation, and the absence of a framework for selecting or combining multiple SSMs.

Age differences in driver visual behavior and vehicle control when driving with in-vehicle and on-road deliveries of service logo signs

With the advances in vehicle technologies, more information is communicated in real-time to the driver via an in-vehicle interface. In-vehicle messaging may deliver safety-related information such as warnings as well as non-safety-related information such as an upcoming lodging place. While much research has focused on the design of messaging safety-related information, little is known about the best practice in in-vehicle messaging of non-safety-related information. This study investigated the effects of information source and load on driver signage logo identification, glance behavior, and vehicle control among younger, middle-aged and older drivers. The logos were presented on: (1) an on-road sign panel, (2) an in-vehicle display, or (3) a combination of both, with half of the drives showing logo only, and the other half of the drives showing logo plus additional text. The general findings support the use of in-vehicle displays, especially when it is presented simultaneously with on-road signs. In-vehicle displays did not lead to a higher workload or more visual distraction, and simultaneous presentations resulted in slightly better speed control. The findings also showed minimal negative impacts on logo identification from increased information load. Older drivers performed less well in signage identification and vehicle control, and they made longer glances to logo information suggesting design considerations should be made to accommodate specific driver characteristics.

Coordinated control of ESC with active front steering and active suspension normal force control for better vehicle’s dynamic response

Advances in vehicle technologies brought changes into architectures of integration of chassis control systems. Alongside other improvements, integration can provide better vehicle handling, stability and safety. Different chassis control systems can use different control methods. Considering while in motion vehicle has movements in longitudinal, lateral and vertical direction, different control systems target different motion. If there is no coordination among active control systems, interaction and performance conflict can arise. This paper presents coordination of three control systems: electronic stability control (ESC), active front steering control (AFSC) and active suspension normal force control (ASNF). ESC is actually direct yaw and anti-roll control using selective wheel braking. ASNF is considered only on front axle. All systems use fuzzy-logic as a control method. Vehicle is presented as 14-DOF nonlinear full vehicle model and for the control purposes 3-DOF reference model was introduced. Emphasis was given on vehicle parameters characterizing dynamic behavior in longitudinal and lateral direction. Benefits of coordinated action of the three systems can be seen from the results gained through simulation of cornering event and single lane change in Matlab/Simulink. Coordinated control adds to the action of ESC itself, resulting in improved stability and handling, and thus safety of the vehicle.

Senior Americans’ perceptions, attitudes, and safety concerns toward Autonomous Vehicles (AVs)

Introduction: Autonomous vehicles (AVs) are considered a promising solution to improve seniors’ safety and mobility. However, to transition to fully automated transportation, especially among seniors, it is vital to assess their perception and attitude toward AVs. This paper investigates seniors’ perceptions and attitudes to a wide range of AV options from the perspective of pedestrians and users in general, as well as during and after the COVID-19 pandemic. Underlying this objective is to examine older pedestrians’ safety perceptions and behaviors at crosswalks in the presence of AVs. Method: A national survey collected data from a sample of 1,000 senior Americans. Using Principal Component Analysis (PCA) and Cluster Analysis, three clusters of seniors were identified with different demographic characteristics, perceptions, and attitudes toward AVs. Results: PCA findings revealed that “risky pedestrian crossing behavior,” “cautious pedestrian crossing behavior in the presence of AVs,” “positive perception and attitude toward shared AVs,” and “demographic characteristics” were the main components explaining most of the variation within the data, respectively. The PCA factor scores were used in the cluster analysis, which resulted in the identification of three distinctive groups of seniors. Cluster one included individuals with lower demographic scores and a negative perception and attitude toward AVs from the perspective of users and pedestrians. Clusters two and three included individuals with higher demographic scores. Cluster two included individuals with a positive perception toward shared AVs from the user perspective, but a negative attitude toward pedestrian-AV interaction. Cluster three included those with a negative perception toward shared AVs but a somewhat positive attitude toward pedestrian-AV interaction. The findings of this study provide valuable insights to transportation authorities, AV manufacturers, and researchers regarding older American’s perception and attitude toward AVs as well as their willingness to pay and use Advanced Vehicle Technologies.

Speeding Behavior when Using Automation: A Descriptive Analysis of Naturalistic Driving Data

Speeding is a prevalent and risky behavior. This study leveraged the MIT Advanced Vehicle Technology naturalistic driving data to identify and characterize speeding behavior across automation levels including Manual Driving, Adaptive Cruise Control (ACC), and Pilot Assist (PA) that supports both longitudinal and lateral vehicle control. We analyzed 146 hours of motorway driving from 15 participants who each drove an instrumented vehicle for one month. Speeding prevalence, magnitude, and duration distributions were compared across automation levels using logistic mixed effect models. Findings indicated that although PA was the most prevalent driving state during free flow motorway driving, drivers were more likely to speed with ACC compared to during Manual Driving or with PA. Automation, ACC and PA, were associated with longer speeding durations and lower speeding magnitudes compared to driving manually. These findings can inform the development of automation systems that may help reduce speeding behaviors and promote safe driving.

Modeling Driver-Automation Interaction using A Naturalistic Multimodal Driving Dataset

This paper explores a holistic approach to understanding the impacts of on-road factors on continuous driver-automation interaction in naturalistic environments. We processed and synchronized CAN Bus signals, GPS coordinates, and high-definition videos collected from two drivers in 98 trips (~97 hours) involving Tesla Autopilot engagement collected over three years as part of the MIT Advanced Vehicle Technology naturalistic driving study. Bayesian generalized linear models were trained with the synchronized data and revealed that steering wheel rotations and speed changes, even to a small extent, cause Autopilot disengagement. Road types and experience were also associated with drivers’ probability of using driver assistance, while speed and surrounding vehicles had little impact (when traveling in stable states). This data-driven approach enables a more comprehensive understanding of driver-automation interaction to enhance safety.

Characterizing driver speeding behavior when using partial-automation in real-world driving

Objective: Speeding is a prevalent and complex risky behavior that can be affected by many factors. Understanding how drivers speed is important for developing countermeasures, especially as new automation features emerge. The current study seeks to identify and describe types of real-world speeding behaviors with and without the use of partial-automation. Methods: This study used a combination of supervised and unsupervised data analysis techniques to assess relevant factors in real-world speeding epochs, extracted from the MIT Advanced Vehicle Technology Naturalistic Driving Study, and classified them into distinct speeding behaviors. Speeding epochs were defined as traveling at least 5 mph over the speed limit for a minimum duration of 3 s. Vehicle speed-exceedance profiles were characterized over time using Dynamic Time Warping and included in multivariate models that evaluated the associations between different features of the speeding epochs, such as speeding duration and magnitude. Finally, the identified features were used to cluster speeding behaviors using the Gower dissimilarity measure. Results: The analysis yielded four types of behaviors in both partially-automated and manual driving: (i) Incidental speeding (low duration, low magnitude), (ii) Moderate speeding (low duration, moderate magnitude), (iii) Elevated speeding (moderate duration, high magnitude), and (iv) Extended speeding (long duration, high magnitude). When comparing the behaviors with and without partial-automation use, both Incidental and Moderate speeding were found to have significantly longer durations with partial-automation than manual driving. Elevated speeding was found to be more prevalent and associated with higher magnitudes during manual than with partially-automated driving. Finally, although Extended speeding was more prevalent during automation use, it was associated with a lower mean and maximum speed magnitude compared to Extended speeding during manual driving. Conclusions: This work highlights the variability in speeding behavior between and within partially-automated and manual driving. The design of systems that mitigate risky speeding behaviors should consider targeting divergent behaviors observed between manual and automated driving as a mechanism to mitigate the prevalence of the different behaviors associated with each state.

ALTERNATIVE FUELS - TRENDS AND PROSPECTS

Aspects of the use of different types of alternative fuels that will ensure economic stability and reduce the harmful effects on the environment are considered. More than a dozen alternative fuels are in production or under development for use in alternative fuel vehicles and advanced technology vehicles. Government and private-sector vehicle fleets are the primary users for most of these fuels and vehicles, but individual consumers are increasingly interested in them. Using alternative fuels and advanced vehicles instead of conventional fuels and vehicles helps conserve fuel and lower vehicle emissions. The world is preparing to shift to a new version of the future that releases us from a reliance on fossil fuels for transportation. Most passenger cars will switch to using batteries and electric motors.

New needs to consider during accident analysis: Implications of autonomous vehicles with collision reconfiguration systems

Autonomous vehicles are equipped with advanced vehicle technology (AVT) that will improve road traffic safety and reduce accidents. However, due to the uncertain behavior of other road users, collisions can never be completely eliminated. Collision reconfiguration systems offer a solution by, for instance, changing where vehicles are hit and how the impact force is directed towards them. Unfortunately, the logic behind the decision-making of collision reconfiguration systems is fundamentally different from that of other AVTs. Fundamentally different feedback might thus be required from accident analyses to ensure the successful design of collision reconfiguration systems. Through simulations, this study explores decision-making strategies of collision reconfiguration systems to ascertain the implications of which feedback is required from accident analyses. Results show that different strategies can be statistically significantly different from each other in the way they affect severity; and that a new source of unobserved heterogeneity could easily be small variations in the algorithms used by collision reconfiguration systems. Based on this, three new needs to consider during accident analysis are put forth: firstly, new safety surrogate measures (SSMs) that consider severity are required; one such SSM is proposed; secondly, to identify new unobserved heterogeneity as a result of collision reconfiguration systems, the trajectories of traffic near-collisions should be recorded, and statistical tools to identify comparable scenarios developed. Thirdly, new collision patterns will make it difficult to analyze the implications of reconfigured collisions, which suggests that collision configurations must be carefully recorded to provide early feedback.

An integrated text mining, literature review, and meta-analysis approach to investigate pedestrian violation behaviours

The goal of this study is to provide an overview of previous research that investigated pedestrian violation behaviour, with a focus on identifying the contributing factors of such behaviour, its impact on pedestrian safety, the mitigation strategies, the limitations of current studies, and the future research directions. To that end, the Latent Dirichlet Allocation (LDA) text mining method was applied to extract a comprehensive list of studies that were conducted during the past 21 years related to pedestrian violation behaviours. Using the extracted studies, a multi-sectional literature review was developed to provide a comprehensive understanding of the different aspects related to pedestrian violations. Afterward, a meta-analysis was undertaken, using the studies that reported quantitative results, in order to obtain the average impact of the different contributing factors on the frequency of pedestrian violations. The study found that pedestrian violations are one of the hazardous behaviours that contribute to both the frequency and severity of pedestrian-vehicle collisions. According to the literature, the waiting time at the curbside, traffic volume, walking speed, pedestrian distraction, the presence of bus stops and schools, and the presence of on-street parking are among the key factors that increase the likelihood of pedestrian violations. The study has also reviewed a wide range of strategies that can be used to mitigate violations and reduce the safety consequences of such behaviour, including simple engineering-based countermeasures, enforcement, solutions that rely on advanced in-vehicle technologies, and infrastructure connectivity features, educational programs, and public campaigns.

Trajectory optimization for connected and automated vehicles in a drop-off area of the departure curbside

Recently, advanced in-vehicle technologies have led to the emergence of connected and automated vehicles (CAVs). CAVs are supposed to improve traffic efficiency and safety by coordinating the vehicles based on the communication among vehicles. This study addresses the trajectory optimization of CAVs in the drop-off area of the departure curbside, which consists of many conflict points. We first propose a centralized control method to optimize the trajectories of CAVs and then propose an implementation procedure to deal with the dynamic features and reduce the problem scales for practical instances. Contrast experiments are conducted to test the performance of the proposed control method. Results under various scenarios (different volumes, safety gaps, and desired speeds) demonstrate that CAVs controlled by the proposed method significantly outperform human-driven vehicles without control concerning mean travel time in the drop-off area.

A Robust Detection Method for Multilane Lines in Complex Traffic Scenes

The robustness and stability of lane detection is vital for advanced driver assistance vehicle technology and even autonomous driving technology. To meet the challenges of real-time lane detection in complex traffic scenes, a simple but robust multilane detection method is proposed in this paper. The proposed method breaks down the lane detection task into two stages, that is, lane line detection algorithm based on instance segmentation and lane modeling algorithm based on adaptive perspective transform. Firstly, the lane line detection algorithm based on instance segmentation is decomposed into two tasks, and a multitask network based on MobileNet is designed. This algorithm includes two parts: lane line semantic segmentation branch and lane line Id embedding branch. The lane line semantic segmentation branch is mainly used to obtain the segmentation results of lane pixels and reconstruct the lane line binary image. The lane line Id embedding branch mainly determines which pixels belong to the same lane line, thereby classifying different lane lines into different categories and then clustering these different categories. Secondly, the adaptive perspective transformation model is adopted. In this model, the motion information is used to accurately convert the original image into a bird’s-eye view image, and then the least-squares second-order polynomial fitting is performed on the lane line pixels. Finally, experiments on the CULane dataset show that the proposed method achieved similar or better performance compared with several state-of-the-art methods, the F1 score of the proposed method in the normal test set and most challenge test sets is better than other algorithms, which verifies the effectiveness of the proposed method, and then the field experiments results show that the proposed method has good practical application value in various complex traffic scenes.

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.