Safety through Disruption (SAFE-D) University Transportation Center (UTC)

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Allusion 2: External Communication for SAE L4 Vehicles
Rossi-Alvarez, Alexandria I.; Klauer, Charlie; Schaudt, Andy; Doerzaph, Zac (Safe-D University Transportation Center, 2023-12)
With SAE Level 4 and above (L4+) Automated Driving Systems (ADSs) being integrated on roadways, stakeholders worldwide are developing external communication systems for other road users to communicate effectively. Most research on SAE L4+ ADS external communication has used simulators or virtual reality platforms to assess driver/road user knowledge, opinions, and attitudes via survey metrics evaluating a single L4 vehicle. However, it is vital to understand perception of external communication in real-world conditions and with multiple SAE L4+ ADSs present. This research explored how the presence of multiple SAE L4+ ADSs with external communication displays affected participants’ crossing decisions. A within-subject design assessed participants’ understanding of SAE L4+ ADS intentions. Results indicated that the presence and condition of external human-machine interfaces (eHMIs) did not influence willingness to cross. It was difficult for participants to focus on the eHMI when multiple vehicles competed for their attention. Participants typically focused on the vehicle that was nearest and most detrimental to their crossing path. Scenario type caused participants to make more cautious crossing decisions but did not influence willingness to cross. This study implies that eHMI with two patterns may still require simplification for pedestrians to interpret in a complicated traffic environment.
Analysis of Advanced Driver-Assistance Systems in Police Vehicles
Zahabi, Maryam; Shahini, Farzaneh; Nasr, Vanessa; Wozniak, David (Safe-D National UTC, 2023-07)
Motor vehicle crashes are the leading cause of death for police officers. Advanced driver assistance systems (ADAS) have the potential to improve officer safety by removing some of the driver’s vehicle control responsibilities. This project included two phases: (1) an ADAS needs and implementation analysis in police vehicles; and (2) an evaluation of police ADAS in a driving simulation study. The first phase included a systematic review of literature and an online survey with officers to understand their ADAS needs and current systems in police vehicles. The second phase evaluated ADAS in high-demand situations using a high-fidelity driving simulator. Results indicated that officer behaviors and opinions on ADAS features were influenced by the trust officers had in the available ADAS, as well as other key factors such as ADAS training and perceived usefulness. ADAS features, including forward collision warning, automatic emergency braking, and blind spot monitoring had a positive effect on police officers' driving performance and in reducing workload. The outcomes of this project provide guidelines regarding effective ADAS features/types to automotive companies supplying police vehicles and can improve officer safety in police operations.
Analysis of an Incentive-Based Smartphone Application for Young Drivers
Henk, Russell H.; Munira, Sirajum; Tisdale, Stacey (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-01)
Traffic crashes remain the leading cause of unintentional youth deaths and injuries across the United States. Development of new and innovative interventions continues, with the aim of addressing this public health issue for the high-risk youth driving population. This report shares results associated with an incentive-based smartphone application (app) developed by the Texas A&M Transportation Institute as part of the peer-to-peer safe driving program, Teens in the Driver Seat®. One of the core features of the app is a reward system, in which drivers earn points for miles driven without any phone interaction. Points earned can be redeemed for rewards and are used as a basis for competitions and achievement of safe driving levels. This project examines data collected from two distinct smartphone app deployments—one in 2017 and one in 2018 —each over a timespan of several months. The datasets included over 12,200 trips and more than 100,000 miles logged using the app. Statistical analyses were performed to assess the influence of incentives on the frequency of distracted driving. Statistically significant reductions in distracted driving (at the 95% confidence level) were shown to have occurred when incentives were awarded for distraction-free driving. Several other data points of interest are presented herein as well.
Analyzing Highway Safety Datasets: Simplifying Statistical Analyses from Sparse to Big Data
Lord, Dominique; Geedipally, Srinivas Reddy; Guo, Feng; Jahangiri, Arash; Shirazi, Mohammadali; Mao, Huiying; Deng, Xinwei (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-07)
Data used for safety analyses have characteristics that are not found in other disciplines. In this research, we examine three characteristics that can negatively influence the outcome of these safety analyses: (1) crash data with many zero observations; (2) the rare occurrence of crash events (not necessarily related to many zero observations); and (3) big datasets. These characteristics can lead to biased results if inappropriate analysis tools are used. The objectives of this study are to simplify the analysis of highway safety data and develop guidelines and analysis tools for handling these unique characteristics. The research provides guidelines on when to aggregate data over time and space to reduce the number of zero observations; uses heuristics for selecting statistical models; proposes a bias adjustment method for improving the estimation of risk factors; develops a decision-adjusted modeling framework for predicting risk; and shows how cluster analyses can be used to extract relevant information from big data. The guidelines and tools were developed using simulation and observed datasets. Examples are provided to illustrate the guidelines and tools.
Assessing Alternate Approaches for Conveying Automated Vehicle ‘Intentions’
Basantis, Alexis; Miller, Marty; Doerzaph, Zachary R.; Neurauter, Luke (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-05)
One of the biggest highly automated vehicle (HAV) market barriers may be a lack of user trust in the automated driving system itself. Research has shown that this lack of faith in the system primarily stems from a lack of system transparency while the vehicle is in motion—users are not informed how the car will react in an upcoming scenario—and not having an effective way to control the vehicle in the event of a system failure. This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to first appear and where the user has less training on and control over the vehicle. To improve user trust and perceptions of comfort and safety, this study evaluated human-machine interface (HMI) systems, focused on visual and auditory displays, to better relay the perceived driving environment and the automated vehicle “intentions” to the user. These HMI systems were then implemented into a HAV developed at the Virginia Tech Transportation Institute and tested with volunteer participants on the Smart Roads.
Automated Shuttles and Buses for All Users
Turnbull, Katherine F.; Higgins, Laura; Gick, Brittney N. (Safe-D University Transportation Center, 2023-12)
Numerous demonstrations and deployments of automated shuttles and buses are occurring in downtown areas, university campuses, business and medical parks, and entertainment complexes throughout the United States. This research project focused on ensuring that individuals with disabilities have equal and safe access to automated shuttles and buses to improve their mobility. The project introduced individuals with disabilities to an automated shuttle in Arlington, TX and a Smart Intersection in College Station, TX, assessing their safety perceptions and obtaining information on any safety concerns about their complete trip. The project identified enhancements in planning, vehicles, service and operations, and the street system and built environment to ensure that individuals with disabilities can safely access and use automated shuttles. The research included interviewing individuals with disabilities before and after riding in the Arlington automated shuttles and online interviews of the Texas A&M University students using mobility devices obtaining feedback on the Smart Intersection and automated shuttles. Virtual and in-person workshops were held examining possible automated shuttle routes on the Texas A&M University–San Antonio campus and the adjacent VIDA development, and the Texas A&M University System RELLIS campus in Bryan. Guidelines for enhancing automated shuttles for individuals with mobility and visual impairments were developed.
Automated Truck Mounted Attenuator: Phase 2 Performance Measurement and Testing
Vilela, Jean Paul Talledo; Mollenhauer, Michael A.; White, Elizabeth E.; Vaughn, Elijah W. (Safe-D University Transportation Center, 2023-12)
Truck-Mounted Attenuators (TMAs) are energy-absorbing devices added to heavy shadow vehicles to provide a mobile barrier that protects work crews from errant vehicles entering active work zones. In mobile and short duration operations, drivers manually operate the TMA, keeping pace with the work zone as needed to function as a mobile barrier protecting work crews. While the TMA is designed to absorb and/or redirect the energy from a colliding vehicle, there is still significant risk of injury to the TMA driver when struck. TMA crashes are a serious problem in Virginia, where they have increased each year from 2011 (17 crashes) to 2014 (45 crashes), despite a decrease in the number of active construction sites between 2013 and 2014. Although various efforts have been made to improve TMA vehicle crashworthiness (e.g., by adding interior padding, harnesses, and supplemental head restraints), the most effective way to protect TMA drivers may be to remove them from the vehicle altogether. Recent advances in automated vehicle technologies—including advanced sensing, high-precision differential GPS, inertial sensing, advanced control algorithms, and machine learning—have enabled the development of automated systems capable of controlling TMA vehicles. Furthermore, the relatively low operating speeds and platoon-like operating movements of leader-follower TMA systems make an automated control concept feasible for a variety of mobile and short-duration TMA use cases without the cost or complexity of full autonomy. This project seeks to develop an automated control system for TMA vehicles using a short following distance, leader-follower control concept which will remove the driver from the at-risk TMA.
Autonomous Delivery Vehicle as a Disruptive Technology: How to Shape the Future with a Focus on Safety?
Das, Subasish; Tsapakis, Ioannis; Wei, Zihang; Elgart, Zachary; Kutela, Boniphace; Vierkant, Valerie; Li, Eric (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-09)
The National Highway Traffic Safety Administration recently granted permission to deploy low-speed autonomous delivery vehicles (ADVs) on roadways. Although the mobility of ADVs is limited to low-speed roads and these vehicles are occupantless, frequent stops and mobility among residential neighborhoods cause safety- related concerns. There is consequently a need for a comprehensive safety impact analysis of ADVs. This study examined the safety implications and safety impacts of ADVs by using novel approaches. This research prepared several datasets such as fatal crash data, aggregated ADV trips and trajectories, and real-world crash data from the scenario design for an ADV-related operational design domain. Association rules mining was applied to the datasets to identify significant patterns. This study generated a total of 80 association rules that provide risk patterns associated with ADVs. The rules can be used as prospective benchmarks to examine how rule-based risk patterns can be reduced by ADVs that replace human-driven trips.
Autonomous Emergency Navigation to a Safe Roadside Location
Furukawa, Tomonari; Zuo, Lei; Parker, Robert G.; Yang, Lisheng (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-11)
In this project, we developed essential modules for achieving the proposed autonomous emergency navigation function for an automated vehicle. We investigated and designed sensing solutions for safe roadside location identification, as well as control solutions for autonomous navigation to the identified location. Sensing capabilities are achieved by advanced fusion algorithms of 3D Lidar and stereo camera data. A novel control design, based on dynamic differential programming, was developed to efficiently plan navigation trajectories while dealing with computation delay and modelling errors. Preliminary validation of proposed solutions was carried out in a simulated environment. The results show strong potential for success, especially for the control module. Hardware integration in a real vehicle has been ongoing in a parallel fashion to enable field tests of developed modules in future work. Key sensing equipment was installed and calibrated and used to collect data for offline analysis. The retrofitting of the vehicle’s actuation mechanism was finished with the whole drive-by-wire system in place. Future work will involve road testing the developed systems.
Autonomous Vehicles for Small Towns: Exploring Perception, Accessibility, and Safety
Li, Wei; Ye, Xinyue; Li, Xiao; Dadashova, Bahar; Ory, Marcia G.; Lee, Chanam; Rathinam, Sivakumar; Usman, Muhammad; Chen, Andong; Bian, Jiahe; Li, Shuojia; Du, Jiaxin (Safe-D University Transportation Center, 2023-09)
As of 2021, there were 18,696 small towns in the US with a population of less than 50,000. These communities typically have a low population density, few public transport services, and limited accessibility to daily services. This can pose significant challenges for residents trying to fulfill essential travel needs and access healthcare. Autonomous vehicles (AVs) have the potential to provide a convenient and safe way to get around without requiring human drivers, making them a promising transportation solution for these small towns. AV technology can become a first-line mobility option for people who are unable to drive, such as older adults and those with disabilities, while also reducing the cost of transportation for both individuals with special needs and municipalities. The report includes our research findings on 1) how residents in small towns perceive AV, including both positive and negative aspects; 2) the impacts of ENDEAVRide—a novel “Transport + Telemedicine 2-in-1” microtransit service delivered on a self-driving van in central Texas—on older adults’ travel and quality of life; and 3) the potential safety implications of AVs in small towns. This report will help municipal leaders, transportation professionals, and researchers gain a better understanding of how AV deployment can serve small towns.
Behavior-Based Predictive Safety Analytics Phase II
Miller, Andrew M.; Sarkar, Abhijit; McDonald, Tony; Ghanipoor-Machiani, Sahar; Jahangiri, Arash (Safe-D National UTC, 2023-06)
This project addressed the emerging field of behavior-based predictive safety analytics, focusing on the prediction of road crash involvement based on individual driver behavior characteristics. This has a range of applications in the areas of fleet safety management and insurance, but may also be used to evaluate the potential safety benefits of an automated driving system. This project continued work from a pilot study that created a proof-of-concept demonstration on how crash involvement may be predicted on the basis of individual driver behavior utilizing naturalistic data from the Second Strategic Highway Research Program. The current project largely focuses on understanding and identifying the risks from a driver based on their driving behaviors, personal characteristics, and environmental influences. This project analyzed large scale continuous naturalistic data as well as event data to study the role of different driving behaviors in the buildup of risk related to a safety-critical event or crash. This research can be used structure the development of real-time crash risk that accounts for those identified driver behaviors to be evaluated across the contextualized information on a roadway.
Behavior-based Predictive Safety Analytics – Pilot Study
Engström, Johan; Miller, Andrew M.; Huang, Wenyan; Soccolich, Susan A.; Machiani, Sahar Ghanipoor; Jahangiri, Arash; Dreger, Felix; de Winter, Joost (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-04)
This report gives an overview of the main findings from the Behavior-based Predictive Safety Analytics – Pilot Study project. The main objective of the project was to investigate the possibilities of developing statistical models predicting individual driver crash involvement based on individual driving style, demographic and behavioral history variables, using large sets of naturalistic driving data. The project was designed as a pilot project with the objective of providing the basis for a future more comprehensive research effort. Based on Second Strategic Highway Research Program (SHRP2) data, a subset of behavior and crash data including 2,458 drivers was created for analysis. The data were analyzed to investigate to what extent these drivers were differentially involved in crashes and near crashes, to what extent this was associated with individual characteristics, and if it is possible to predict individual drivers’ crash and near crash involvement based on variables representing individual characteristics. The results clearly demonstrated the presence of differential crash and near crash involvement and showed significant associations between enduring personal factors and crash involvement. Moreover, logistic regression and random forest classifiers were relatively successful in predicting crash and near crash involvement based on individual characteristics, but the ability to specifically predict involvement in crashes was more limited.
Behavioral Indicators of Drowsy Driving: Active Search Mirror Checks
Meyer, Jason E.; Llaneras, Robert E. (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-07)
Driver impairment due to drowsiness or fatigue has a significant impact on the safety of all road users. Assessing an impairment such as driver drowsiness through the use of vehicle-based technology continues to be an area of interest. Both the initial detection and continued monitoring of driver drowsiness have been the emphasis of vehicle-based driver monitoring systems (DMS). Particularly, in-vehicle eye tracking systems have been implemented as a way of determining driver state. Specifically, when hands-free driving assistance features are engaged, measures such as the driver’s percentage of eye closure (PERCLOS) are being considered to determine driver drowsiness. However, one challenge of such a metric is its reliability, particularly with regard to false alarms (when a DMS indicates the driver is drowsy but in fact is not). Therefore, the use of more gross-level driver behavioral measures may serve as a way of cross-checking the assessments of a DMS. This work mined an available dataset in order to examine driver search behavior, with the goal of identifying relationships between driver vigilance and drowsy driving, to test the hypothesis that driver search behavior (e.g., mirror checks) degrades with increasing levels of drowsiness. Based on a statistical comparison of participant driving data encompassing instances of alert, moderately drowsy, and drowsy driving, no significant differences were observed among these three classifications.
Big Data Visualization and Spatiotemporal Modeling of Risky Driving
Jahangiri, Arash; Marks, Charles; Machiani, Sahar Ghanipoor; Nara, Atsushi; Hasani, Mahdie; Cordova, Eduardo; Tsou, Ming-Hsiang; Starner, Joshua (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-07)
Statistical evidence shows the role of risky driving as a contributing factor in roadway collisions, highlighting the importance of identifying such driving behavior. With the advent of new technologies, vehicle kinematic data can be collected at high frequency to enable driver behavior monitoring. The current project aims at mining a huge amount of driving data to identify risky driving behavior. Relational and non-relational database management systems (DBMSs) were adopted to process this big data and compare query performances. Two relational DBMSs, PostgreSQL and PostGIS, performed better than a non-relational DBMS, MongoDB, on both nonspatial and spatial queries. Supervised and unsupervised learning methods were utilized to classify risky driving. Cluster analysis as an unsupervised learning approach was used to label risky driving during short monitoring periods. Labeled driving data, including kinematic information, were employed to develop random forest models for predicting risky driving. These models showed high prediction performance. Open source and enterprise visualization tools were also developed to illustrate risky driving moments in space and time. These tools can be used by researchers and practitioners to explore where and when risky driving events occur and prioritize countermeasures for locations in highest need of improvement.
Building Equitable Safe Streets for All: Data-Driven Approach and Computational Tools
Dadashova, Bahar; Zhu, Chunwu; Ye, Xinyue; Sohrabi, Soheil; Brown, Charles; Potts, Ingrid (Safe-D University Transportation Center, 2023-09)
Roadway safety in low-income and ethnically diverse U.S. communities has long been a major concern. This research was designed to address this issue by developing a data-driven approach and computational tools to quantify equity issues in roadway safety. This report employed data from Houston, Texas, to explore (1) the relationship between road infrastructure and communities’ socioeconomic and demographic characteristics and its association with traffic safety in low-income, ethnically diverse communities and (2) the type of driver behaviors and characteristics that affect crash risks in underserved communities. The team first built an inclusive road infrastructure inventory database by employing remote sensing and image processing techniques. Then, the relationship between communities’ socioeconomic and demographic characteristics and traffic safety was investigated through the lens of road infrastructure characteristics using data mining, deep learning tools, and statistical and econometric models. Clustering analysis was used to uncover the role in underserved communities of socioeconomic and demographic characteristics of drivers and victims involved in crashes. Structural equation models were then used to explore the association between neighborhood disadvantage, transportation infrastructure, and roadway crashes. Findings shed light on road safety inequity and sources of these disparities among communities using data-driven methods.
Characterizing Level 2 Automation in a Naturalistic Driving Fleet
Perez, Miguel A.; Terranova, Paolo; Metrey, Mariette; Bragg, Haden; Britten, Nicholas (Safe-D University Transportation Center, 2024-01)
Introducing automation into the vehicle fleet disrupts how vehicles operate and potentially affects what drivers do with these features and expect from vehicle performance. Therefore, it is imperative to study driver adaptations in response to these innovations. This investigation leveraged 47 vehicles from the Virginia Tech Transportation Institute Level 2 (L2) Naturalistic Driving Study to analyze driver behavior with L2 automation features. Results showed no sizeable differences between periods of L2 feature usage and general driving periods with respect to time-of-day and calendar-related metrics. Most L2 feature usage occurred on motorways, following design expectations. L2 features were activated for 7.2 minutes in trips lasting an average of 22.8 minutes, or about 32% of the L2 trip duration. Driver-initiated overrides were predominantly done by braking or accelerating the vehicle, with steering-based overrides being minimal and likely involving lane changes without using a turn signal. Intervention requests were the most common takeover request, followed by requests due to insufficient driver hand contact with the steering wheel. Findings suggest that as L2 features penetrate the U.S. fleet in non-luxury consumer vehicles, system usage will be common and comparable with previous findings for luxury offerings. While evidence of potential system misuse was observed, future work may further operationalize system misuse and assess the prevalence of such behaviors.
Connected Vehicle Data Safety Applications
Martin, Michael; Wu, Lingtao; Ramezani, Mahin; Li, Xiao; Turner, Shawn; Stutes, Sophia; Hasan, Faiza; Potter, Michael (2023-09)
The large-scale assessment of how driving behavior affects traffic safety and ongoing surveillance is hindered by data collection difficulties, small sample sizes, and high costs. Connected vehicles (CV) now offer massive volumes of observed driving behavior data from newer vehicles with myriad electronics and sensors that monitor the state of the vehicle, environmental conditions, and the driver’s actions. This project evaluated the viability of CV data in roadway safety applications with the objective of improving existing predictive crash methods, measuring traffic speed and its relationship to crashes, and determining whether CV data could be used to evaluate pavement marking products. The research team developed safety performance functions (SPFs) for rural two-lane segments and urban intersections in Texas. The results showed that the SPFs improved with the addition of hard braking and hard acceleration counts in a majority of areas. Further, a variety of CV speed measures were generated from the CV data and were shown to have conflicting correlations with crash risk and counts. Lastly, the research team developed the data processing methods for evaluating pavement marking products but was unable to perform an evaluation due to the lack of detailed construction project records.
Connected Vehicle Information for Improving Safety Related to Unknown or Inadequate Truck Parking
Katsikides, Nicole; Gick, Brittney N.; Parab, Smruti; Hwang, William "Billy"; Lee, Dahye; Montes de Oca, Jose Rivera; Farzaneh, Reza; Kong, Xiaoqiang "Jack"; Srisan, Tat; Bell, Stephen; Alden, Andy S.; Warner, Jeff; Schrank, David (Safe-D National UTC, 2022-10)
Safety issues due to commercial truck parking shortages are a national concern. National hours-of-service (HOS) regulations limit drivers’ time on the road to increase safety by limiting fatigue. This requires drivers to locate safe, secure, and legal parking wherever they are when or before they hit their limits. If drive time is exhausted with no nearby truck parking, drivers may park in unsafe or unauthorized locations to meet HOS requirements, or they may continue to drive while fatigued. As a result, there are intrinsic safety implications to all highway users due to large trucks parking in unsafe locations or truck drivers driving past their allotted hours. With the projected growth of truck traffic, the demand for adequate truck parking will continue to outpace the supply of public and private parking facilities. The current study will help transportation agencies develop solutions to the parking availability problem by identifying effective methods for using data to estimate truck parking demand and areas of parking opportunity, assessing available data sources for estimating truck parking demand and supply, and determining the safest solutions for distributing information on parking availability directly to drivers.
Cooperative Perception of Connected Vehicles for Safety
Eskandarian, Azim; Ghorai, Prasenjit; Nayak, Anshul (Safe-D National UTC, 2023-04)
In cooperative perception, reliably detecting surrounding objects and communicating the information between vehicles is necessary for safety. However, vehicle-to-vehicle transmission of huge datasets or images can be computationally expensive and often not feasible in real time. A robust approach to ensure cooperation involves relative pose estimation between two vehicles sharing a common field of view. Detecting the object and transferring its location information in real time is necessary when the object is not in the ego vehicle’s field of view. In such scenarios, reliable and robust pose recovery of the object at each instant ensures the ego vehicle accurately estimates its trajectory. Once pose recovery is established, the object’s location information can be obtained for future trajectory prediction. Deterministic predictions provide only point estimates of future states which is not trustworthy under dynamic traffic scenarios. Estimating the uncertainty associated with the predicted states with a certain level of confidence can lead to robust path planning. This study proposed quantifying this uncertainty during forecasting using stochastic approximation, which deterministic approaches fail to capture. The current method is simple and applies Bayesian approximation during inference to standard neural network architectures for estimating uncertainty. The predictions between the probabilistic neural network models were compared with the standard deterministic models. The results indicate that the mean predicted path of probabilistic models was closer to the ground truth when compared with the deterministic prediction. The study has been extended to multiple datasets, providing a comprehensive comparison for each model.
Countermeasures to Detect and Combat Inattention While Driving Partially Automated Systems
Paras, Carolina Rodriguez; Ferris, Thomas (Safe-D University Transportation Center, 2023-09)
Vehicle manufacturers are introducing increasingly sophisticated vehicle automation systems to improve driving efficiency, comfort, and safety. Despite these improvements, partially and fully automated vehicles introduce new safety risks to the driving environment. Driver inattention can contribute to increased risk, especially when control transfers from automation to the human driver. To combat inattention and ensure safe and timely transitions of control, this study investigated the effectiveness of a vehicle cuing system that engages different sensory modalities (e.g., visual, auditory, and tactile) and both simple and complex cue messages to announce the need for manual takeover. Twenty-four participants completed a driving simulator study involving scripted driving sections with and without partial automation. Participants navigated six scripted automation failure events, some preceded by takeover cues. Measures of driving performance, safety, secondary task performance, and physiological indices of workload did not differ significantly based on display type or complexity. However, a clear trend showed that, compared to events not associated with takeover cues, driver reaction time to automation failure is substantially faster when preceded by cues of any type or complexity. This study provides evidence of the benefit of supporting driver situational awareness, safety, and performance by issuing cues and guiding drivers in taking control when the vehicle system predicts a likely automation failure.

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