INTRODUCTION
SAVE-IT is a comprehensive program featuring over fifteen tasks, each comprising two research phases and a literature review phase, with extensive documentation available online at www.volpe.dot.gov/hf/roadway/saveit/docs.html This document aims to provide a concise summary of the overall SAVE-IT program, offering an index to the detailed task documents while illustrating how the research from these tasks was integrated into a final system tested in driving simulators, test tracks, and real roadways As part of Task 15, which focuses on Program Summary and Benefit Evaluation, this document highlights key data that indicate the potential benefits of SAVE-IT technologies in reducing collisions and enhancing driver acceptance.
This report will be organized as follows:
Overview of the SAVE-IT program, describing the programmatic structure and what goals the SAVE-IT program was attempting to achieve
Summary of the implemented countermeasures and the building blocks that supported adaptation
Adaptive Safety Warning Benefits Summary
Summary of data that evaluates the potential benefit of the adaptive safety warning (forward collision warning and lane departure warning systems that adapt to the driver’s head pose) systems
Summary of data that evaluates the potential benefit of the distraction mitigation (countermeasures that attempt to reduce distraction) systems
Summary and concluding observations and a discussion of lessons learned and future research needs
To effectively index the outcomes of the component tasks, this document will denote a SAVE-IT task document by using the task number in superscript format (e.g., Task¹).
4 Distraction Mitigation 4 ) Thus, references numbers 1 through 14C will be reserved for the respective SAVE-IT tasks References thereafter (15 and up) will be in the order in which they are cited.
SAVE-IT PROGRAMMATIC OVERVIEW
Program Goals
The SAVE-IT program aimed to showcase effective adaptive interface technologies that minimize distraction-related accidents and improve the efficiency of collision avoidance systems Its initial proposal outlined several sub-goals to achieve this mission.
1 Advance the deployment of adaptive interface technology as a potential countermeasure for distraction-related crashes.
2 Enhance collision warning system effectiveness by optimizing onset algorithms tailored to the driver’s level of distraction.
3 Conduct human factors research to help derive distraction measures for use in algorithms for triggering interface adaptation.
4 Develop and apply evaluation procedures for assessment of SAVE-IT safety benefits.
5 Provide the public with documentation of the human factors research and with information describing the algorithms for controlling the driver vehicle interface to the extent needed for specifying performance and standardization requirements.
6 Identify potential scalable system concepts and sensing technologies for further stages of research and development as follow on to this SAVE-IT program.
Programmatic Structure and Funding
The SAVE-IT program was structured in two main phases: Phase 1 focused on developing essential components of the SAVE-IT system over 1.5 years, involving six diagnostic measures and adaptive countermeasures, as well as crash scenario identification and concept demonstration Phase 2 was divided into two sub-phases; Phase 2A, lasting two years, integrated these components into a working prototype vehicle and driving simulator, while Phase 2B, spanning 1.5 years, evaluated both the vehicular and simulator versions of the SAVE-IT systems A detailed overview of the tasks, participating institutions, and task leaders is provided in Table 1.
The SAVE-IT Program, funded primarily by the National Highway Transportation Safety Administration (NHTSA) with approximately $3 million, aimed to enhance transportation safety Additional funding was provided by Delphi, contributing around $2.4 million, and Ford, which offered about $200,000 in in-kind research support Importantly, the commercial partners did not receive any government funding, ensuring that the entire $3 million was dedicated to research conducted by university partners The program was administered by the Volpe Center, a division of the Research and Innovation Technology Administration (RITA).
Figure 1 The Phases of the SAVE-IT research program
The SAVE-IT vehicles are showcased in two images: the left features the Phase 1 SAVE-IT concept demonstration, while the right highlights the prototype vehicle assessed during Phase 2B testing.
Table 1 SAVE-IT Tasks, Institution, and Task Leads
1 Scenario Identification UMTRI David Eby
2 Driving Task Demand UMTRI Paul Green
4 Distraction Mitigation University of Iowa John Lee
5 Cognitive Distraction University of Iowa John Lee
6 Telematics Demand University of Iowa John Lee
7 Visual Distraction Delphi Harry Zhang
8 Intent / Maneuver Prediction Delphi Matthew Smith
9 Safety Warning Countermeasures Delphi Matthew Smith
10 Technology Development Delphi Greg Scharenbroch
11 Data Fusion Delphi Matthew Smith
12 Standards and Guidelines University of Iowa John Lee
13 System Integration Delphi Ray Prieto
14A Evaluation: Driving Simulator U of Iowa (NADS) Timothy Brown
14B Evaluation: Driving Simulator Ford (VIRTTEX) Jeff Greenberg
14C Evaluation: Test track and On-road UMTRI David Leblanc
Research Strategy
In order to conduct this research within the constraints of the overall budget, the SAVE-
The IT program conducted a thorough assessment of the strengths and weaknesses of three testing venues: driving simulators, test tracks, and field testing To ensure a viable production solution, it is essential to meet affordability requirements while also addressing the effectiveness of collision reduction and user acceptance Table 2 illustrates the research constraints related to evaluating collision-reduction effectiveness alongside acceptance evaluation.
To effectively assess the collision-reduction capabilities of different systems, it is essential to place drivers in high-risk situations, as the absence of collisions limits safety improvement opportunities While extensive on-road testing over millions of miles is impractical, constrained scenarios can enhance the likelihood of real or virtual crashes Driving simulators offer researchers a valuable tool to safely expose drivers to high-risk virtual collision environments, allowing for effective evaluation without compromising driver safety.
Because of the high level of efficiency in producing these collision scenarios, one- exposure experimental (between-subject) designs become practical, which avoids the
In the United States, police-reported collisions occur at a rate of 2.0 per million miles When drivers face unexpected situations with a high risk of collision, their behavior may be altered in response to future events To effectively evaluate the impact of collision-reduction strategies, it is essential to conduct studies on a between-subjects basis, ensuring that each driver encounters only one surprise-collision event.
The between-subjects methodology, while requiring a large number of participants, can be optimized for shorter individual testing times Building on Delphi's driving simulator research from the ACAS FOT program, the SAVE-IT program enhanced this methodology to create an efficient protocol for evaluating the effectiveness of various collision warnings This approach effectively measured the differences in human-machine interfaces and various warning adaptation methods.
Table 2 The Research Constraints for Collision-reduction effectiveness vs Acceptance.
Research Constraints Collision-reduction effectiveness Acceptance
Central Question Which system compromises safety the least or provides the greatest safety benefit?
Which system is least annoying, most acceptable, most preferred, or provides the greatest perception of benefit?
Exposure Requirements Expose drivers to real or simulated threatening scenarios where there is an apparent danger of collision
Expose drivers to a representative experience that balances favorable and unfavorable aspects of the system
Control Requirements Control driver expectations Facilitate adequate driver comprehension of the system
Participant Requirements Between-subjects design Span representative demographics
(Reaction times, time to collision, collision rates and velocities)
Subjective Measures (Annoyance, perceived effectiveness, buy likelihood, preference)
More participants More time per participant
Ideal Research Facility Driving Simulator On-road
Drivers exposed to imminent events in driving simulators often provide limited and biased feedback, making it difficult to gauge the acceptance of collision-reduction systems To better understand acceptance, on-road field testing is essential, as it captures a diverse range of participants across various demographics, such as age and gender This broader subject pool is crucial since acceptance can significantly differ among individuals Additionally, acceptance testing should offer a balanced experience that highlights both the positive and negative aspects of the system Field testing not only serves as an ideal method for assessing acceptance but also allows for evaluation under diverse conditions, which can reveal critical system-environment performance aspects that may have been overlooked.
The Advanced Collision Avoidance Systems Field Operational Test (ACAS FOT) program yielded limited insights into its effectiveness in reducing collisions due to insufficient mileage to anticipate any incidents Despite this, the extensive field testing, which covered over one hundred thousand miles, offered valuable information regarding user acceptance and the functionality of forward collision warning systems in real-world driving conditions.
Human-subject testing on a test track offers a balance between the realism of on-road testing and the controlled environment of driving simulators, allowing for effective examination of collision-reduction methods While the test track provides a more authentic setting than simulators, it can induce heightened arousal in drivers, making them feel uneasy and impacting their driving behavior The need for communication with traffic control and the novelty of the environment can overwhelm participants, hindering their ability to relax and drive naturally However, prolonged exposure to test conditions may help mitigate these challenges, enabling more accurate assessments of driver responses.
The SAVE-IT program employed a research strategy that balanced realism and control in its evaluations On-road field testing was utilized to assess acceptance, emphasizing a high level of realism with less controlled events Conversely, for evaluating collision-reduction effectiveness, a driving simulator was chosen to prioritize control over realism To find a middle ground, a test track was implemented, serving as a bridge between on-road evaluations and simulator testing Given the brief nature of SAVE-IT exposures and the limited alerts each driver received, the test track was instrumental in educating drivers about the balance between collision-reduction effectiveness and potential nuisance before they experienced the systems on actual roadways.
The SAVE-IT program utilized various environments, including driving simulators, test tracks, and on-road venues, to assess their impact on realism and control levels These settings were instrumental in investigating the acceptance of new technologies and their effectiveness in reducing collisions.
SAVE-IT SYSTEM SUMMARY
Overview of SAVE-IT Systems
The SAVE-IT system is illustrated in Figure 4, showcasing its conceptual framework It features two main branches of countermeasures: Adaptive Warnings and Distraction Mitigation These branches utilize adaptive inputs from two key sources: Head Pose and Driving Task Demand, ensuring an effective response to driver behavior and situational demands.
Figure 4 Summary of the SAVE-IT countermeasures as a function of adaptation inputs
Adaptive Warnings aim to minimize the adverse impacts of driver distraction, while Distraction Mitigation strategies focus on directly alleviating distraction itself One example of Adaptive Warnings is the Adaptive Forward Collision Warning system, which enhances driver awareness and safety.
(AFCW) and Adaptive Lane Departure Warning (ALDW), which are like their non- adaptive counterparts, except that they also respond to the driver’s Head Pose
The SAVE-IT program did not investigate driver drowsiness, even though adaptations for AFCW and ALDW systems would be necessary to address it Instead, Forward Collision Warning (FCW) and Lane Departure Warning (LDW) were prioritized for adaptation, as collision analyses revealed that driver distraction significantly influenced incidents these systems aim to mitigate Distraction Mitigation strategies include the Trip Report, which offers post-drive safety feedback, and Adaptive Infotainment Availability and Advisory, which restricts or advises against certain infotainment features when driving demands are high Additionally, Adaptive Phone Management automatically sends calls to voicemail when the system is in auto-screen mode, enhancing safety during driving.
Task Demand is too high for the phone to be answered safely
Figure 5 displays the vehicle architecture that supported the SAVE-IT systems This information is described in more detail in other SAVE-IT reports 10, 13
Figure 5 SAVE-IT vehicle architecture.
Data Fusion and System Integration
The implementation aimed to create a straightforward system that drivers could easily understand and evaluate during Phase 2B Building on previous driving simulator experiments, Phase 2A involved on-road drives to observe adaptive interface behavior and gather diverse subjective measures This iterative process enabled immediate cycles of implementation, testing, and refinement based on both subjective and objective data As novice drivers interacted with the system in real-world conditions, it became evident that countermeasures needed to be clear and comprehensible; complex solutions with hidden assumptions were poorly received Consequently, the focus shifted towards simplicity and transparency in the SAVE-IT countermeasures, leading to the abandonment of more intricate strategies in favor of simpler, more effective decision-making techniques.
The SAVE-IT system underwent streamlining, resulting in the exclusion of Cognitive Distraction 5 due to limitations in developing effective countermeasures for Cognitive Distraction 4 and 9 Current automotive-grade hardware lacks the capability to detect cognitive distraction accurately, as it requires precise eye position discrimination that is not feasible with affordable technology While research-grade apparatus can detect cognitive distraction, a reliable system for the automotive market is unlikely to emerge in the next decade Additionally, there are no effective countermeasures that drivers would accept for managing cognitive distraction Notably, research revealed that cognitive distraction interacts with visual distraction, significantly affecting driver responses to predictable events, such as a lead vehicle braking when cues are present, while having a lesser impact on reactions to unpredictable events.
The Intent/Maneuver Prediction task 8 was discontinued at the conclusion of Phase 1 due to its need for advanced sensing technology This decision streamlined the program, allowing for increased focus on the emerging content during Phase 2 of the SAVE-IT initiative.
Driver Monitoring Building Block
During the SAVE-IT program, Delphi developed a production-intent Driver State Monitor (DSM) that employs a single camera and advanced vision-processing techniques to assess the driver's head pose Unlike traditional research-oriented systems that necessitate manual training, this automatic system functions without any intervention from the driver or experimenter Additionally, the DSM has the capability to detect eye closures for drowsiness monitoring, although this feature was not utilized within the scope of the SAVE-IT program.
Figure 6 Delphi’s Driver State Monitor (DSM) camera and illuminators (left) and output (right)
Detecting eye gaze using affordable automotive-grade technology presents significant challenges, despite advancements in driver monitoring systems Research indicates that while eye-gaze measures account for slightly more variance in driver performance metrics, such as accelerator release time (ART) and standard deviation of lane position (SDLP), head pose remains a reliable indicator of driver distraction, particularly for SDLP Non-forward glances that do not alter head pose are generally less severe than those that do Drivers tend to combine head and eye movements for comfort, making head pose monitoring a more practical solution for technology implementation Consequently, the SAVE-IT program opted for a head-pose-monitoring driver safety system, as it effectively identifies critical visual distractions.
Figure 7 Correlations between head pose or eye gaze and performance measures:
Accelerator Release Time (ART) and Standard Deviation of Lane Position (SDLP)
The DSM system defines a non-forward head pose as occurring when a driver's head position deviates more than +/- 20 degrees from the forward-facing position This threshold is based on findings from the 100-Car Study, which indicated an increased risk when drivers glance between 20 and 40 degrees Additionally, on-road data suggests that during long drives, drivers may slightly turn their heads (less than 20 degrees) while using their eyes to maintain focus ahead The system tracks the occurrence and duration of non-forward head poses, integrating this data into adaptive warnings and documenting it in the trip report for the driver to review at the end of their journey.
Driving Task Demand Building Block
The driving task demand building block assesses the attention required from drivers based on their driving environment, which can vary significantly In clear weather with minimal traffic and wide lanes, the driving task may require little attention, while poor weather and heavy traffic can demand full concentration Initial efforts to quantify driving task demand using crash statistics proved unfeasible, leading to the use of subjective assessments instead Vehicle data was collected by driving an instrumented vehicle across diverse roads and traffic conditions, resulting in one hundred 8-second video segments that showcased varying levels of traffic, road types, and driving maneuvers Ten participants, evenly split by gender, were recruited to watch these segments and provide feedback.
To effectively detect unexpected events and hazards, manage vehicle speed and lane positioning, and prevent crashes and road departures, rate your level of attentiveness on a scale from 1 to 7, where 1 indicates low attention and 7 signifies high attention.
The subjective responses obtained from participants were analyzed alongside sensor data collected from the vehicle during specific driving segments, leading to the development of a driving task demand algorithm This algorithm incorporated various data points, including radar target metrics (such as range, time headway, and angle to target), lane tracking vision processing (lane width), and host vehicle parameters (yaw rate, speed, and brake pedal depression) The correlation between predicted demand levels and median subjective demand ratings was strong, with a coefficient of 0.79 Notably, in 75% of the analyzed videos, the demand levels generated by the algorithm aligned with the median subjective ratings, demonstrating the algorithm's effectiveness.
Examination of the mismatches showed that most occurred with borderline cases and that the rating cut-off values could be adjusted to generate better matches (up to 93% match).
Subjects also provided judgment on visual distraction feedback and on demand-based advisories for IVIS functions when they were asked:
“In your judgment, which of the following features should be 1 (allowed), 2
(advised not to be used), or 3 (disallowed) when driving under the condition that is portrayed by the video clip?”
The study revealed a correlation between median demand ratings and judgments of visual distraction, indicating that higher driving demand ratings lead to increased concerns about infotainment functions It was suggested that drivers should avoid using infotainment systems during high-demand driving tasks These insights were utilized to establish thresholds for adaptive infotainment advisories While the Driving Task Demand task 2 aimed to create detection methods, the Data Fusion task 11 ultimately developed and implemented the Driving Task Demand algorithm used in the SAVE-IT system due to delays in the former's completion.
Adaptive Safety Warning Countermeasures
A warning system that fails to consider the driver's state cannot accurately gauge their level of attention, as drivers may fluctuate between being fully attentive and distracted Research using driving simulators shows that when distractions occur, drivers' reaction times to unexpected events can exceed 3 seconds However, a forward collision warning algorithm cannot rely on a fixed 3-second response time, as this would lead to excessive alerts that annoy drivers and undermine their trust in the system Conversely, assuming that drivers are always attentive could minimize unnecessary alerts but may leave distracted drivers unable to react in time when a critical warning is issued.
Designers of non-adaptive warning systems face the challenge of balancing the need to give distracted or impaired drivers adequate response time while avoiding irritation for attentive drivers who do not require warnings In contrast, adaptive warning systems aim to resolve this dilemma by more accurately determining when a driver actually needs a warning.
Adaptive warnings are based on the principle that attentive drivers do not require warnings, as highlighted by the 100-Car Study, which indicates that crashes often occur due to sudden, unexpected situations coinciding with lapses in attention In 11 out of 15 lead-vehicle crashes, drivers had their eyes off the road just before or during the critical moments leading to the collision Research by Dingus et al suggests that inattention significantly hampers drivers' ability to respond and avoid accidents This underscores the notion that lack of focus on the roadway is a crucial factor in lead-vehicle and single-vehicle crashes, potentially more significant than traditional crash statistics suggest.
According to Dingus et al (2019), crashes predominantly occur when drivers fail to adequately respond to precipitating events due to various contributing factors The model illustrated in Figure 8, derived from the 100 Car Study, highlights that crashes often happen when issues like inattention or adverse weather disrupt a driver's ability to react to sudden events, such as a lead vehicle unexpectedly braking In scenarios where these contributing factors are absent, drivers typically manage to avoid collisions, resulting in near-misses instead However, when factors like visual distractions are present, they can significantly impair a driver's response, transforming a near-miss into a collision Other potential catalysts that may compromise driver responses include poor roadway conditions, mechanical failures, and impairments from fatigue or alcohol.
Figure 8 Simple conceptual model of how inattention is a catalyst for crashes
This model simplifies the complexities of driver behavior by focusing solely on their reactions to events, neglecting their proactive risk-reduction strategies It suggests that without distractions, drivers typically do not gain much from warnings, as attentive drivers are already aware of impending dangers In situations where a driver is focused on the road, they usually detect and respond to threats effectively, leaving little room for improvement through warnings Even in cases where drivers may be slower to react due to factors like age or intoxication, warning systems often fail to accelerate their response, as drivers tend to confirm threats independently before taking action.
The SAVE-IT program has created a preliminary list of adaptation strategies for adaptive warnings, identifying the most promising candidates for further evaluation Among these strategies are Differential Display Location and others, which are detailed in Table 3.
Differential Display Modalities, Differential Alert Timing, and Alert Suppression
Differential Display Location and Differential Display Modalities adaptations change the driver-vehicle interface, while Differential Alert Timing and Alert Suppression adaptations alter the algorithms for generating alerts Specifically, the Differential Display Location adaptation places the visual alert stimulus at the location of the driver's current visual distraction.
Differential Modalities adaptation enhances driver safety by delivering more immediate stimuli when distractions occur In contrast, Alert Suppression adaptation effectively stops alerts from triggering when the driver is focused Additionally, Differential Alert Timing adaptation adjusts alert generation, offering earlier notifications during distractions and delaying alerts when the driver is attentive.
Table 3 Negative and Positive Warning Adaptation Strategies
Attention Forward Attention Not-forward
Goal: Improve Acceptance Goal: Improve
Non-Adaptive Nominal Alert Nominal Alert
Differential Location Nominal Alert Visual alert in location of driver’s attention
Differential Stimuli Less intrusive or urgent stimuli More intrusive or urgent stimuli
Alert Suppression No Alert Nominal Alert
Adaptations in driving warning systems can be classified as either negative or positive, impacting driver attention differently Negative adaptations, found in the “Attention Forward” category, aim to reduce unnecessary alerts, enhancing driver acceptance by softening warnings when attention is focused on the road Conversely, positive adaptations in the “Attention Not-Forward” category seek to amplify alerts when the driver’s focus is diverted, ultimately aiming to improve collision reduction While their primary goals differ, driver acceptance and safety benefits are interconnected; effective warning systems require a level of driver trust to ensure compliance Research by Lees and Lee highlights that frequent false alerts can lead to decreased compliance with forward collision warning systems, illustrating the detrimental "cry wolf" effect on driver response to alerts.
Mitsopoulos 24 ) Furthermore, it is likely that if the system apparently fails to achieve a safety benefit, drivers may be less likely to accept the system because they do not perceive the system as being useful, thus degrading acceptance.
The adaptation strategies that were implemented for the SAVE-IT evaluation will be described separately for the forward collision warning and lane departure warning systems.
Adaptive Forward Collision Warning (AFCW)
Adaptive Forward Collision Warning (AFCW) systems, unlike traditional Forward Collision Warning (FCW) systems, leverage Delphi's forward-looking radar to assess potential collisions with the lead vehicle while also incorporating instantaneous head pose data to tailor alert timing based on the driver's attentiveness The final configuration for the Adaptive FCW tested during the SAVE-IT evaluation is outlined in Table 4, following an exploration of various adaptation alternatives presented in Table 3.
The Differential Alert Timing strategy has emerged as a highly effective solution for Forward Collision Warning (FCW) systems, addressing the challenge of balancing timely alerts for distracted drivers with minimizing unnecessary notifications during attentive driving In its non-adaptive mode, the algorithm presumes a brake reaction time of 2.5 seconds To enhance driver acceptance, the Adaptive FCW (AFCW) algorithm reduces this time to 0.5 seconds when the driver's head is facing forward, effectively suppressing unnecessary alerts Conversely, when the driver's head is turned away from the roadway, the AFCW system generates earlier alerts, starting with a 1.5-second reaction time that gradually increases to a maximum of 3.5 seconds if the driver remains distracted for over 2 seconds.
Table 4 Adaptive and Non-Adaptive mode Forward Collision Warning as a Function of Driver Head Pose
The SAVE-IT non-adaptive Forward Collision Warning (FCW) algorithm is designed to issue alerts earlier than standard production systems, enhancing driver awareness during limited on-road testing in the evaluation phase This approach facilitates a comparison between the adaptive system and a highly sensitive FCW system, which prioritizes safety benefits over minimizing driver annoyance.
Adaptive Lane Departure Warning (ALDW)
ADAPTIVE MODE Dependent on DSM Head Pose
NON- ADAPTIVE MODE ADAPTIVE MODE
Dependent on DSM Head Pose
The Advanced Lane Departure Warning (ALDW) system, utilizing Delphi's forward-looking camera and vision processing, detects lane positions and alerts drivers when they cross lanes Research indicates that traditional Lane Departure Warning (LDW) systems offer limited benefits when drivers are focused on the road To enhance effectiveness, ALDW suppresses alerts when the driver’s head is facing forward Additionally, it distinguishes between brief head movements, such as mirror checks, and prolonged periods of distraction Alerts are triggered only if the driver’s head has been turned away for over 2 seconds, with at least 1 second occurring before a lane crossing, reflecting an understanding that drivers likely recognize imminent lane changes if they glance away momentarily This approach is backed by findings from the 100-Car Study.
Table 5 Adaptive and Non-Adaptive mode Lane Departure Warning as a Function of Driver Head Pose
For more detailed information on how the adaptive and non-adaptive systems operated,refer to the Adaptive Safety Warning Countermeasures report 9
Distraction Mitigation Countermeasures
Crashes often occur when drivers fail to maintain adequate attention during unexpected events that necessitate a quick, non-automatic response Attention can be compromised by distractions or impairments, such as drowsiness or substance use, which reduce the driver's focus on the task at hand Effective driving demands that a driver's attention aligns with the level of driving complexity and environmental unpredictability In low-demand scenarios, like a straight, traffic-free country road during the day, drivers can afford to divert their attention, as they can anticipate forthcoming events In contrast, high-demand situations, such as navigating a multi-lane winding road, require heightened attention to ensure safety.
ADAPTIVE MODE Dependent on DSM Head Pose
NON- ADAPTIVE MODE ADAPTIVE MODE
Dependent on DSM Head Pose erratic traffic) situations require more attention because during any time attention is diverted, there is a high probability that a novel response may be required.
A safety system designed to reduce infotainment distractions must effectively balance the driver's attention between the driving task and the surrounding environment In scenarios with low driving demands, diverting attention to non-driving tasks may not pose significant safety risks However, in high-demand driving situations, these distractions can critically impair focus on driving The primary objective of the distraction mitigation system is to ensure that attention to driving remains above the necessary levels dictated by the current driving conditions For instance, during low to moderate driving demands, some distraction may be manageable, but high-demand situations necessitate undivided attention to the driving task.
Figure 9 Attention allocation to driving as a function of driving task demand
The three methods for mitigating distraction (Trip Report, Adaptive Infotainment
Availability and Advisory, and Adaptive Phone Management) will be described separately.
A delayed feedback mechanism, presented as a trip report at the end of the drive, replaces real-time driver distraction alerts This report includes the driver's percentage of forward head-pose along the trip route and an overall average attention level, using color coding for easier interpretation Additionally, it highlights the number and location of Forward Collision Warning (FCW) and Lane Departure Warning (LDW) alerts during the drive While the implementation of this countermeasure would be voluntary, it aims to pique the driver's curiosity by also displaying the total miles driven and gas mileage for the trip.
Research from SAVE-IT indicates that post-drive feedback is more effective in enhancing safety compared to instantaneous feedback While concurrent feedback does not accelerate driver responses during braking incidents, post-drive feedback significantly improves reaction times This method has the advantage of not interfering with immediate driving tasks, allowing it to focus on long-term behavior change by educating drivers on safe driving practices and risk reduction Additionally, since drivers often quickly forget near incidents, trip reports can help refresh their memories and better align their self-assessment with actual performance.
At the end of their drives, drivers receive a trip report that highlights key metrics, including a color-coded percentage indicating the degree of forward head pose during the trip Additionally, the report details the number and locations of Forward Collision Warning (FCW) and Lane Departure Warning (LDW) alerts encountered throughout the journey.
As driving demands escalate, drivers may need to limit infotainment functions to focus on the road This is illustrated in Table 6, which outlines the logic behind these advisories To signify this reduction in functionality, the buttons associated with these infotainment features are highlighted in a dim amber color, distinct from their usual brightness.
The phrase "Use Cautiously" is prominently displayed beneath the page heading to inform drivers about the adaptation's purpose As driving demands increase, certain complex infotainment features are disabled to enhance safety To minimize confusion, the interface employs a gray-out effect, a familiar element in computer graphical user interfaces, effectively linking it to the explanatory text below the page description.
Figure 11 Task demand-based advisory and lockout for IVIS features
Table 6 Driving task demand-based advisories and lock-outs of IVIS functions
IVIS Task Driving Task Demand
In park Low Medium High
Radio Tuning No Advisories No Advisories No Advisories No Advisories
Satellite Radio No Advisories No Advisories No Advisories Advisories
CD No Advisories No Advisories No Advisories Advisories
MP3 No Advisories No Advisories Advisories Advisories
Phone Dialing No Advisories No Advisories Advisories Lock-out
Navigation POI No Advisories No Advisories Advisories Lock-out
Navigation Map Reading No Advisories No Advisories Advisories Lock-out
Navigation Turn-by-turn No Advisories No Advisories Advisories Advisories
Text Messaging No Advisories Advisories Lock-out Lock-out
Figure 12 Advisories (amber) and lockouts (gray) for creating text messages
Adaptive Phone Management is a tailored approach to phone usage during driving, designed to address the increased perceived risk associated with phone functions as driving task demands rise Research indicates that drivers prefer self-initiated mitigation methods over those imposed by systems To accommodate this preference, drivers can choose from three phone screening options: no screening, do-not-disturb, and automatic screening The automatic screening feature intelligently assesses driving task demands to determine whether calls should be directed to the driver or sent to voicemail The no-screening mode ensures that all calls, including potential emergencies, reach the driver, while the do-not-disturb option sends all calls to voicemail, allowing drivers to manage their attention based on anticipated driving demands or to enjoy uninterrupted quiet.
Figure 13 The three driver-selected modes of phone management Only the auto mode (right) is responsive to the instantaneous assessment of driving-task demand
To streamline the evaluation process, the infotainment and phone systems in SAVE-IT were designed as simulations rather than fully operational devices The interactive interface was capable of playing MP3 files, but it did not support external communication outside the vehicle.
Warning Human Machine Interface
The SAVE-IT program aimed to enhance human-machine interfaces for Forward Collision Warning (FCW) and Lane Departure Warning (LDW) systems by creating a cost-effective visual display for alerts Instead of the conventional icon on a heads-up display (HUD), the program introduced an "exogenous display" that utilizes quick red flashes of light in the driver's forward field of view These brief flashes, lasting only a fraction of a second, effectively capture the driver's attention without distracting them, allowing the external scene to convey the potential threat.
The ACAS FOT program highlights the challenges faced by distracted drivers when responding to collision threats, as illustrated in Figure 15 Without warnings, drivers may take several seconds to notice a danger, and while visual icons can interrupt distractions, they introduce an additional step where drivers must interpret the warning Since drivers often distrust such warnings, they tend to rely on their own observations before taking action Although optimal icon presentation can reduce distraction time, it still delays the response by several hundred milliseconds An alternative approach involves using an "exogenous display" that captures the driver's attention by creating a visual disturbance in their peripheral field, prompting them to look where the threat is most likely to occur This method aims to redirect focus away from the disturbance itself, with the ideal display being a brief, flashing red LED programmed to blink three times at 5 Hz with a 50% duty cycle.
A study using a driving simulator found that the exogenous display resulted in significantly faster accelerator release times compared to a HUD icon display Due to its simplicity and low cost, the exogenous display presents a more favorable option for production implementations than a HUD Consequently, the exogenous display was chosen as the visual interface for both the FCW and LDW systems in the SAVE-IT program The combination of auditory, visual, and haptic stimuli for the FCW and LDW alerts is detailed in Table 7.
Figure 14 The exogenous display: a quick flash of light in the center of the forward roadway
Figure 15 Conceptual diagram of a distracted driver responding to an imminent event
The study examines the relationships between Accelerator Release Time (ART), Brake Reaction Time (BRT), and Time to Collision (TTC) under different conditions, specifically focusing on the impact of an audible tone combined with three visual displays: no visual display, an icon on a Head-Up Display (HUD), and an exogenous display.
Table 7 Auditory, visual, and haptic stimuli used for the FCW and LDW alerts
Warning System Auditory cues Visual cues Haptic cues
Emulation of rumble strip from speakers on relevant B-pillar Exogenous Visual Display Directional haptic vibration on driver seat pan
Warning Short tone sequence from forward speakers Exogenous Visual Display None
ADAPTIVE SAFETY WARNING BENEFITS SUMMARY
Collision-reduction potential
Driving Simulator Forward Collision Warning Results 9
Adaptive warnings are designed with the understanding that attentive drivers do not benefit from alerts, suggesting that reducing or delaying warnings may not jeopardize safety A study employed a between-subjects design to examine the effectiveness of three warning methods—visual, visual plus auditory tone, and no warning—while also considering the impact of visual distractions The findings, illustrated in Figure 17, reveal the accelerator release times and crash velocities across these different conditions, highlighting the potential of negative adaptation strategies in reducing collisions.
Figure 17 Accelerator Release Time (s) and Collision Velocity (m/s) as a function of warning type (Visual-plus-Auditory, Visual only, and No warning) Percentages represent crash rates
The findings illustrated in Figure 17 highlight the behavior of adaptive Forward Collision Warning (FCW) systems, particularly in non-distracted scenarios where two negative adaptation strategies are evident The absence of warnings for attentive drivers exemplifies the Alert Suppression strategy, while the use of visual-only alerts aligns with the Differential Stimuli strategy Notably, the data indicates that FCW alerts significantly assist distracted drivers but offer minimal advantage to those who are attentive This suggests that completely suppressing FCW alerts or only maintaining visual cues may not jeopardize safety, assuming accurate assessment of driver attention to the roadway Additionally, while not covered in the SAVE-IT program, monitoring driver impairment, such as drowsiness, is essential for future production systems.
The Alert Suppression strategy, while not directly applied to FCW, resembles an intensified version of the Differential Timing strategy As the timing for non-distraction events is delayed, the likelihood of resolving conflict events before reaching critical thresholds increases This data supports the notion of negative adaptation, indicating that attentive drivers do not gain advantages from warnings, thereby suggesting that such negative adaptations may not diminish the potential for reducing collisions.
The Differential Timing strategy for Advanced Forward Collision Warning (AFCW) combined both positive and negative adaptations to enhance driver safety The strategy aimed to improve acceptance through late alerts for attentive drivers while providing early alerts for distracted drivers to reduce collision risks A study comparing distracted drivers who received early alerts—1 second sooner than standard alerts—showed that these drivers braked 0.85 seconds earlier (p < 0.01), resulting in an increased minimum time to collision by 0.83 seconds (p = 0.09) Notably, while two drivers in the nominal alert group experienced crashes, no crashes occurred in the early alert group These findings suggest that earlier warnings for distracted drivers significantly enhance collision-reduction potential.
The SAVE-IT program assessed the collision-reduction capabilities of Advanced Forward Collision Warning (AFCW) on test track 14C During a series of driving tests, participants were instructed to input a navigation destination into the SAVE-IT infotainment system In two instances, the lead vehicle surrogate unexpectedly decelerated at a rate of 0.4 g while the subjects were engaged in these tasks To ensure genuine surprise, only the 27 cases where drivers did not glance up before the alert were analyzed.
The study found a statistically significant difference in brake reaction time (BRT) between drivers using Advanced Forward Collision Warning (AFCW) and those using standard Forward Collision Warning (FCW), with AFCW drivers responding an average of 0.9 seconds faster (p < 0.05) These test track experiments demonstrate that the SAVE-IT mechanisms offer inattentive drivers extra time to react in potential forward crash situations.
Figure 18 The surrogate target used in the Task 14C test track evaluation
Figure 19 Test track evaluation of driver responses to the lead vehicle braking event.
Alert Rates and Acceptance
Drivers on the test track experienced various braking events with a focus on adaptation for both Adaptive Forward Collision Warning (AFCW) and Adaptive Lane Departure Warning (ALDW) For Lane Departure Warning (LDW), scripted events included non-adaptive nuisance alerts from lane changes and adaptive suppression of these alerts, alongside ALDW alerts triggered by prolonged non-forward head poses In the case of Forward Collision Warning (FCW), drivers faced surprise braking scenarios and non-adaptive alerts from lead vehicle actions, with adaptive suppression also applied After these trials, drivers rated the usefulness of the systems, revealing that while the LDW ratings lacked statistical significance, the FCW ratings showed that AFCW was significantly more useful than traditional FCW, as illustrated in Figure 20.
Figure 20 Usefulness Van der Laan ratings of Adaptive and Non-adaptive FCW and LDW
On-road exposures were conducted in Indiana and Michigan, focusing on different aspects of the SAVE-IT system to enhance participant experience In Indiana, separate tests for Lane Departure Warning (LDW) and Forward Collision Warning (FCW) were performed with a group of 28 naive drivers, ensuring they could concentrate on one system at a time The FCW drives covered approximately 120 miles in conflict-rich small-city areas, while the LDW drives spanned about 150 miles across various highway types Each driver experienced both adaptive and non-adaptive modes during their drives, with the order of modes counterbalanced to minimize bias.
The adaptive versions of Forward Collision Warning (FCW) and Lane Departure Warning (LDW) systems significantly reduced alert frequency, with FCW showing a 70% decrease (from 7.5 to 2.2 alerts per 100 miles) and LDW achieving a remarkable 95% reduction (from 7.8 to 0.4 alerts per 100 miles) A majority of drivers preferred the adaptive mode, citing the lower alert rate as the primary reason Specifically, 10 out of 14 drivers favored adaptive FCW (AFCW), while 12 out of 14 preferred adaptive LDW (ALDW) The findings indicate that the reduced alert frequency was a key factor influencing driver satisfaction, with users reporting that the adaptive systems were more acceptable, generated fewer nuisance alerts, and were less distracting overall.
Figure 21 Preferences for Adaptive vs Non-adaptive FCW and LDW systems
Figures 22 and 23 illustrate the questionnaire results for Forward Collision Warning (FCW) and Lane Departure Warning (LDW) systems, showing a consistent trend in driver expectations Both adaptive and non-adaptive systems met driver expectations, with adaptive systems significantly enhancing the perception of acceptable nuisance alert rates Furthermore, drivers were more likely to recommend the adaptive systems, which also did not negatively impact their perception of safety enhancement.
Figure 22 Subjective Adaptive vs Non-Adaptive responses for FCW
Figure 23 Subjective Adaptive vs Non-Adaptive responses for LDW
A group of 12 out of 26 test-track drivers who previously encountered unexpected braking events participated in a subsequent study involving 2000 miles of real-world driving They drove an 84-mile route twice, once in the morning and once in the afternoon, with each journey consisting of approximately half freeway driving and half a combination of major and minor arterials across urban, suburban, and rural settings The study examined the effects of the vehicle's systems by alternating between adaptive and non-adaptive modes during the drives, with the order of mode presentation counterbalanced among participants Notably, unlike previous tests in Indiana, drivers in this study experienced both Lane Departure Warning (LDW) and Forward Collision Warning (FCW) systems simultaneously.
Figure 24 illustrates the alert rates for both adaptive and non-adaptive versions of Forward Collision Warning (FCW) and Lane Departure Warning (LDW) The suppression rates are slightly lower than those reported in Indiana, with Adaptive Lane Departure Warning (ALDW) achieving an 88% suppression rate and Adaptive Forward Collision Warning (AFCW) at 60% Notably, nine out of eleven drivers experienced fewer alerts during AFCW compared to FCW, while all eleven drivers benefited from the adaptive reduction in LDW.
Figure 24 Suppression rates for Adaptive FCW and LDW systems in the Michigan drives
The study on Forward Collision Warning (FCW) systems revealed that adaptive suppression tends to prioritize more bothersome alerts Analysis indicated that while only one third of FCW alerts occurred in same-lane scenarios, two thirds of Adaptive FCW (AFCW) alerts were same-lane events This suggests that the alerts retained by the AFCW system are more beneficial for drivers, whereas the suppressed alerts are often viewed as nuisances.
Figure 25 Suppression rates for Adaptive FCW in the Michigan drives by category
During the adaptive drive, if the FCW system had operated in non-adaptive mode, the driver would have received 71 alerts instead of 23 A review found that none of the 53 alerts suppressed by adaptation were deemed useful The AFCW system introduced five additional alerts, which occurred when the host vehicle approached the lead vehicle before a lane change, resulting in a total of 18 alerts shared between the AFCW and FCW systems However, the reduction in alerts significantly outweighed the additions, leading to an overall decrease in alerts Among the 18 common alerts, the AFCW system presented four earlier, three simultaneously, and eleven later than the FCW system.
Despite positive feedback from both objective and subjective data, drivers showed no clear preference between adaptive and non-adaptive systems, with most (6 of 11) expressing a desire for both options in their vehicles Preferences were evenly split, with two drivers favoring each system and one opting for "other." While Indiana drivers preferred the adaptive mode, Michigan drivers' simultaneous experience with the SAVE-IT systems may have led to confusion Additionally, Indiana participants were limited to choosing only one system, which could explain their stronger preference Nonetheless, there are indications that a preference for the adaptive system may emerge over time.
In a recent survey, participants expressed a stronger preference for the Adaptive Forward Collision Warning (AFCW) system over the Forward Collision Warning (FCW) system for their next vehicle, with average agreement ratings of 1.27 for AFCW compared to 1.69 for FCW Analysis of the responses indicated that the significant difference was primarily due to a notable number of subjects who "strongly disagreed" with wanting the non-adaptive FCW system, while no participants expressed disagreement regarding the AFCW system, although two out of twelve subjects did "strongly disagree" about wanting FCW.
Figure 26 Agreement whether drivers want Adaptive and Non-adaptive systems on next cars
The results for subjective measures of Lane Departure Warning (LDW) reveal that participants reported a higher frequency of unnecessary warnings from LDW (average rating of 3.25) compared to Adaptive Lane Departure Warning (ALDW), which was rated closer to "never" at 1.25 Additionally, the level of annoyance caused by these unnecessary alerts was significantly greater for LDW (average rating of 2.25) than for ALDW, which was rated near "none" at 1.09 Upon reviewing alert event videos from their drives, drivers acknowledged that alerts were considerably more useful for ALDW.
“mildly agree”: 2.16) than LDW (near “agree/disagree equally”: 3.27)
Figure 27 Subjective ratings for Adaptive and Non-adaptive LDW.
DISTRACTION MITIGATION BENEFITS SUMMARY
Collision-reduction potential
Phase 1 driving-simulator research revealed that locking drivers out of a visually- distracting task can improve driver responses to lead vehicle braking events, revealing a statistically significant 1.87-s longer minimum time to collision when drivers were locked out of the visually-distracting task compared with when they were free to engage in the task 4 However, the strategy of advising drivers against engaging in the visually- distracting task did not reveal any measurable benefit The collision-reduction effectiveness of the adaptive phone management was not evaluated in the SAVE-IT program.
The SAVE-IT program's Phase 1 and 2A investigated the impact of real-time feedback on driver distraction, revealing that while distraction alerts reduced eyes-off-road glances, they did not lead to fewer collisions and sometimes even increased reaction times to braking events This raised concerns about potential visual distractions from such alerts Consequently, the program shifted focus to post-drive feedback, assessing its effectiveness through a driving simulator The results indicated that combining post-drive feedback with concurrent alerts initially improved driver glance duration, but this effect diminished over time However, both feedback types significantly decreased brake reaction times to lead vehicle braking events after the post-drive feedback was provided By the fourth drive, drivers receiving post-drive feedback responded faster to braking events compared to the baseline group, suggesting that post-drive feedback plays a crucial role in enhancing collision-reduction potential.
Figure 28 Glance duration on road as a function of feedback type (mean and standard error)
Figure 29 Brake reaction time to lead vehicle braking events (mean and standard error)
Acceptance
Phase 2A assessed the acceptance of trip report and distraction alert features in one experiment, while advising and lock-out features were evaluated in another The Van der Laan scale responses indicated that the middle age group (35–55) found the trip report feature more useful compared to other distraction mitigation strategies, with older participants (65–75) showing even more favorable responses Drivers expressed higher levels of usefulness and satisfaction for the trip report feature, while the distraction alert and advising features were perceived as nearly "useless," and the lock-out feature was rated neutrally Although this analysis is qualitative and compares two different studies, it highlights the trip report feature as a more promising strategy for driver acceptance.
The usefulness and satisfaction ratings for the Distraction Alert, Trip Report, Advising, and Lock-out features were assessed in two separate experiments The Distraction Alert and Trip Report ratings were derived from a combined feedback scenario presented to drivers, while the Advising and Lock-out ratings focused specifically on visually-oriented strategy responses from middle-aged drivers.
During the Michigan driving studies, participants rated the safety of tasks such as radio tuning, phone dialing, and destination entry on a five-point scale from "very safe" (1) to "very unsafe" (5) This data aimed to evaluate the correlation between participants' safety perceptions and the responses from the adaptive infotainment feature advisory system Table 8 presents the participants' ratings along with the percentage of individuals who deemed the tasks as "neither safe nor unsafe."
(3), “safe” (4), or “very safe” (5), and the action taken by the adaptive system (allowed, advised against, or locked out).
Table 8 Driver perceptions of the safety of various Infotainment tasks compared with the actions taken by the distraction mitigation system
Allowed Advised Against Locked Out
Advised Against Locked Out Locked Out
Note The percentages in parentheses represent the number of participants who classified the task as not unsafe (between “very safe” and “neither safe nor unsafe”).
The distraction mitigation strategy for advising or limiting certain features aligns well with drivers' perceptions of their safety while multitasking There is a strong correlation between drivers' views and the actions of the distraction mitigation system Infotainment features are permitted when average safety ratings fall below 2.5, indicating a level of perceived safety, while restrictions apply when ratings exceed 2.5 but remain below 3.5.
Infotainment features were restricted when average safety ratings exceeded 3.5, indicating a cautious approach to distraction mitigation Despite a strong correlation between driver perceptions and distraction management, the system may have overreacted to perceived threats For instance, while 76% of participants deemed radio tuning at least moderately safe, the system advised against it during high-demand driving scenarios This suggests that the calibration of distraction interventions might exceed driver preferences, and a more acceptable approach could involve reserving such restrictions for genuinely high-risk combinations of driving demands and infotainment tasks.
CONCLUSIONS
Lessons Learned
The SAVE-IT program established effective protocols for assessing systems aimed at reducing crashes, particularly through its driving simulator test protocol for safety warnings This protocol utilized a between-subjects design, allowing drivers to experience only one imminent event, which minimized the risk of response contamination from prior events This approach also facilitated easier comparisons with previously collected data By streamlining the testing process to under thirty minutes per subject, the program was able to gather sixteen responses in an eight-hour day Initially, challenges arose in synchronizing imminent events with visual distractions, but as the protocol evolved, it successfully surprised participants The refined methodology developed during the SAVE-IT program now serves as a robust framework for future research on systems designed to mitigate frontal collisions.
The between-subjects methodology effectively minimizes the need to surprise drivers multiple times, which was initially thought to be challenging, especially in a high-arousal test track environment However, the UMTRI-developed test track protocol successfully surprised drivers not only once but also on a second occasion, achieving a higher success rate of 59% for the second surprise compared to 47% for the first This improvement may be attributed to drivers becoming more comfortable with the vehicle and test track over time Ultimately, the methodology surprised 27 out of 52 drivers and demonstrated sensitivity to the differences in response times between adaptive and non-adaptive versions of Forward Collision Warning (FCW).
The SAVE-IT program's driving simulator evaluation 14A employed a test methodology that proved to be disappointing To optimize resource use, participants were subjected to a diverse array of systems within a limited timeframe As shown in Table 9, the experimental design for this evaluation was notably complex, highlighting the challenges faced during the study.
Table 9 Order of study drives and events
The high variability in the data significantly contributed to the absence of statistically significant results, likely due to the complexity of the experimental design Participants received numerous alerts—such as lane departure, forward collision, and distraction mitigation warnings—within a brief period of three 10-minute drives, shortly after becoming familiar with the system This close temporal proximity of interactions may have influenced the participants' responses to the system.
In the early stages of data fusion, subjects unfamiliar with collision warning systems faced multiple adaptive systems simultaneously, hindering their understanding of the balance between nuisance and timely alerts Their overwhelming responses prompted a revision of the testing methodology, leading to the separation of evaluations for AFCW, ALDW, and distraction mitigation countermeasures The key takeaway from this experience was the importance of simplicity in study design; rather than consolidating numerous elements into a single extensive test, it is more effective to conduct a series of simpler tests that collectively offer a comprehensive understanding.
Future Research Needs
To enhance driving safety through adaptive infotainment management, it is crucial to address the integration of nomadic devices The AIDE (Adaptive Integrated Driver-vehicle Interface) Program in Europe is actively working on this by uniting nomadic device manufacturers to explore effective integration with vehicle platforms By enabling the driver-vehicle interface to manage nomadic devices, the potential for adaptive infotainment management can significantly increase Conversely, if drivers resist restrictions on infotainment access, they may resort to using nomadic devices that bypass these limitations, potentially undermining the market appeal of adaptive systems.
The trip report in the SAVE-IT program, initially perceived as mandatory, is actually a voluntary tool provided at the end of each drive Drivers often lack the time to fully engage with this information, presenting a challenge in capturing their interest for better understanding To enhance engagement, solutions could include combining fuel economy data with safety information and sending reports via email for convenient review Effective strategies may involve field testing and comprehensive surveys Additionally, concerns about the storage of safety-related data in vehicles could be mitigated by granting drivers ownership of their data, allowing them to delete information or impact data as needed.
The most significant advantage of trip reports emerges when they are linked to tangible incentives for drivers For instance, drivers have previously permitted insurance companies to track their driving habits in return for potentially lower insurance premiums By establishing a clear connection between trip report data—like safety warning events and driver head position—and crash likelihood, insurance companies could offer discounts on premiums, thereby encouraging safer driving through financial incentives.
The SAVE-IT program has uncovered significant insights into adaptive and conventional collision warning systems, yet numerous questions persist despite findings from other studies like the 100 Car Study A crucial area for advancement is the validation of effective driving simulator test protocols that assess collision-reduction potential Initial efforts by SAVE-IT have begun to correlate driving simulator results with real-world collision reduction effectiveness As warning systems become more prevalent, the lack of standardized approaches among various OEMs could confuse drivers and hinder their responses Therefore, there is an increasing need for standardization in the human-machine interface Further research is essential to balance cost, collision-reduction potential, and user acceptance before establishing such standards.
Research into human factors surrounding semi-autonomous vehicles is becoming increasingly vital as these vehicles are set to be permitted on public roads Drivers will assume a supervisory role, needing to intervene in situations that exceed the automation's capabilities, sometimes without prior warning from the system This shift necessitates monitoring drivers more closely and developing effective adaptive mechanisms to enhance driver-automation interaction Understanding the dynamics of semi-autonomous vehicles is essential for creating the next generation of alerts, as the driver's role will evolve alongside increasing automation levels The interaction between collision warnings and autonomous systems will be critical, as seen in current technologies like Forward Collision Warning (FCW) that works with adaptive cruise control For instance, in the ACAS FOT program, the warning algorithm adapted when cruise control was active, focusing on the vehicle's braking authority rather than the driver's reaction time As automation expands, alerts indicating when driver intervention is needed will become increasingly significant.
This study was carried out under the SAVE-IT program, a collaboration between Delphi Electronics and Safety, the University of Michigan Transportation Research Institute (UMTRI), and the University of Iowa, with sponsorship from the U.S Department of Transportation.
The National Highway Traffic Safety Administration (NHTSA) oversees transportation safety through its Office of Vehicle Safety Research, with the program managed by the Volpe Center The authors express their gratitude to Mike Perel and Eric Traube from NHTSA, as well as Mary Stearns and Tom Sheridan from Volpe, for their invaluable assistance and guidance in this initiative.
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