Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-24-063 FEBRUARY 2024

Response to Emergency Vehicles When 

Driving in a Mixed Vehicle Fleet
FOREWORD

Advances in vehicle automation show promise to improve transportation systems management 

and operations and traffic incident management. Ongoing research on how drivers understand, 

trust, and use automated vehicles highlights the influence that human factors will have on vehicle 

automation. Successful transportation networks in the future may depend on a symbiotic 

relationship between connected-automated vehicles (CAVs) and nonconnected vehicles. CAVs 

could potentially allow responders to arrive at traffic incidents more quickly. Little is known 

about how drivers will respond to emergency vehicles in a mixed fleet with different levels of 

CAV market penetration.

This report documents a driving simulator experiment that explores drivers’ behavioral responses 

to emergency vehicles when traveling within a CAV mixed fleet. This report may be of interest 

to State and local transportation agencies and traffic incident management responders wanting to 

understand how CAVs can impact driver behavior in different levels of CAV market penetration. 

Carl K. Andersen

Acting Director, Office of Safety and Operations

Research and Development

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

(USDOT) in the interest of information exchange. The U.S. Government assumes no liability for 

the use of the information contained in this document.

Non-Binding Contents

Except for the statutes and regulations cited, the contents of this document do not have the force 

and effect of law and are not meant to bind the States or the public in any way. This document is 

intended only to provide information regarding existing requirements under the law or agency 

policies.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement. 

Disclaimer for Product Names and Manufacturers

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this document only because they are considered essential to the 

objective of the document. They are included for informational purposes only and are not 

intended to reflect a preference, approval, or endorsement of any one product or entity. 
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-24-063

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Response to Emergency Vehicles When Driving in a Mixed Vehicle 

Fleet 

5. Report Date

February 2024 

6. Performing Organization Code: 

7. Author(s)

Robert Sanchez (ORCID: 0000-0002-0763-6146), Starla Weaver

(ORCID: 0000-0002-9559-8337), Szu-Fu Chao (ORCID: 0000-

0002-2037-5200)

8. Performing Organization Report No.

9. Performing Organization Name and Address

Leidos, Inc.

11251 Roger Bacon Drive

Reston, VA 20190

10. Work Unit No.

11. Contract or Grant No.

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

1200 New Jersey Avenue, SE

Washington, DC 20590

13. Type of Report and Period Covered

Final report; September 2019–April 2023 

14. Sponsoring Agency Code

HRSO-30 

15. Supplementary Notes

Michelle Arnold (HRSO-30; ORCID: 0000-0001-5088-8800), Office of Safety and Operations Research and 

Development, served as the Task Order Manager for the Federal Highway Administration. 

16. Abstract

This study explored the impact of connected vehicle alert messages on drivers’ responses to emergency vehicles. 

The participants drove a simulated vehicle either with or without SAE Level 2 automation (SAE International 

2021). During the drive, an emergency vehicle approached the participant from behind. The participants’ vehicle 

connectivity was manipulated so that half the participants received an in-vehicle alert that an emergency vehicle 

was approaching. Connected-automated vehicle (CAV) market penetration (MP) was also manipulated to create an 

environment where none, some, or all the surrounding traffic responded to a connected vehicle (CV) with a 

vehicle-to-vehicle (V2V) emergency vehicle alert. Vehicle kinematics and eye tracking were used to assess the 

impact of vehicle automation, V2V communication, and CAV MP on drivers’ responses to emergency vehicles. 

The research team found CV alerts to be effective in getting the participants to yield to the approaching emergency 

vehicle. Drivers with CV alerts led to increased pullover rates and reduced speeds to pull over sooner than the 

participants without CV alerts. The participants driving Level 2 vehicles with CV alerts in low or full MP reduced 

speeds much sooner than the participants in no MP. These benefits were greatest in the full MP condition. 

However, in low or full MP, the participants in Level 2 vehicles who received CV alerts reduced speeds slightly 

later than the participants with manual vehicles who received CV alerts. Eye-tracking analyses found that the 

participants receiving CV alerts tended to spend more time gazing at the console and less time gazing at the 

roadway during the period when the emergency vehicle appeared to when the emergency vehicle passed. No 

crashes occurred at any time when the emergency vehicle appeared or when vehicles reentered the roadway. 

Overall, this study’s findings support a promising future of V2V communication for emergency vehicle awareness 

and response and the potential for CAVs to help responders arrive at traffic incidents more quickly.

17. Key Words

Connected vehicles, connected-automated vehicles, emergency 

vehicles, market penetration, mixed fleet, traffic incident 

management, transportation systems management and operations, 

vehicle-to-vehicle

18. Distribution Statement

No restrictions. This document is available to 

the public through the National Technical 

Information Service, Springfield, VA 22161. 

https://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

29

22. Price

N/A 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
ii

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
iii

TABLE OF CONTENTS

FOREWORD................................................................................................................................. 1

CHAPTER 1. INTRODUCTION ................................................................................................ 1

CHAPTER 2. METHOD .............................................................................................................. 5

PARTICIPANTS..................................................................................................................... 5

APPARATUS .......................................................................................................................... 5

Simulator ........................................................................................................................... 5

Simulator Scenario............................................................................................................ 5

Eye-Tracking System ........................................................................................................ 7

EXPERIMENTAL DESIGN.................................................................................................. 7

PROCEDURE ......................................................................................................................... 8

ANALYSIS .............................................................................................................................. 8

CHAPTER 3. RESULTS .............................................................................................................. 9

RESPONSE TO EMERGENCY VEHICLE ........................................................................ 9

Pullover Behavior.............................................................................................................. 9

Response Latency ............................................................................................................ 10

Eye Gaze on the Console ................................................................................................ 11

Eye Gaze on the Road ..................................................................................................... 12

Eye Gaze on Rearview Mirror and Side Mirrors ........................................................ 13

EFFECTS ON DRIVING SAFETY .................................................................................... 15

Distance to Other Vehicles ............................................................................................. 15

Blinker Use ...................................................................................................................... 16

Vehicle Incidents ............................................................................................................. 16

Driving Speed and Variability ....................................................................................... 16

Following Distance .......................................................................................................... 18

Lane Variability .............................................................................................................. 19

CHAPTER 4. DISCUSSION ..................................................................................................... 21

REFERENCES ............................................................................................................................ 23
iv

LIST OF FIGURES

Figure 1. Illustration. Simulated scenario. ...................................................................................... 6

Figure 2. CV alert message. ............................................................................................................ 7

Figure 3. Graph. Mean distance to reduce speed by MP, connectivity, and vehicle automation. 11

Figure 4. Graph. Frequency and proportion of time of console eye gazes by MP, connectivity, 

and vehicle automation. ................................................................................................................ 12

Figure 5. Graph. Proportion of road eye gazes by MP, connectivity, and vehicle automation. ... 13

Figure 6. Graph. Proportion of mirror eye gaze before siren. ....................................................... 14

Figure 7. Graph. Proportion of mirror eye gaze after siren. ......................................................... 14

Figure 8. Graph. Mean distance between vehicles. ...................................................................... 15

Figure 9. Graph. Mean speeds by MP, connectivity, and vehicle automation. ............................ 17

Figure 10. Graph. Speed variability by MP, connectivity, and vehicle automation. .................... 18

Figure 11. Graph. Mean following distance by MP, connectivity, and vehicle automation. ....... 19

Figure 12. Graph. Mean lane variability as a function of MP, connectivity, and vehicle 

automation..................................................................................................................................... 20

LIST OF TABLES

Table 1. Experimental design table................................................................................................. 7

Table 2. Pullover behavior by vehicle automation type, CV type, and MP. .................................. 9

Table 3. Average pullover distance at time emergency vehicle passes. ....................................... 10

Table 4. Blinker use by gender when yielding to the emergency vehicle and returning to 

roadway. ........................................................................................................................................ 16
v 

LIST OF ABBREVIATIONS

CAV connected-automated vehicle 

CV connected vehicle

FHWA Federal Highway Administration 

ITS intelligent transportation system

MP market penetration

TIM traffic incident management

TSMO transportation systems management and operations

V2I vehicle-to-infrastructure

V2V vehicle-to-vehicle

1 

CHAPTER 1. INTRODUCTION

Transportation system management and operations (TSMO) refers to an integrated set of 

strategies designed to optimize the safety and reliability of a road network (Viriyasitavat and 

Tonguz 2012). Transportation agencies use TSMO to reduce congestion and manage the impact 

of nonrecurring events on the traffic network. One area of focus is traffic incident management 

(TIM). Unexpected incidents can reduce both the operational capacity and safety of a roadway. 

Incidents such as crashes, disabled vehicles, or spilled cargo can hamper the flow of traffic 

where the incident is occurring (Owens et al. 2010). Furthermore, travelers who reach the end of 

an unexpected queue or who are distracted by traffic incidents are at risk of being involved in 

secondary incidents. TSMO’s goal is to minimize the impact of an incident. This goal can be 

facilitated by the rapid arrival of TIM responders. In addition to directly helping those who may 

be injured or at risk of injury, TIM responders help guide traffic around the incident and clear the 

roadway (Owens et al. 2010). The prompt arrival of responders can help ensure the safety of 

those directly involved in the incident, minimize the risk of secondary collisions, and reduce the 

negative impact the incident will have on the operation of the transportation system. 

Connected-automated vehicles (CAVs) could potentially help responders arrive at traffic 

incidents more quickly. Multiple traffic simulation studies suggest that automated systems can 

use messages transmitted by connected emergency vehicles to create travel plans that speed the 

response of emergency vehicles while having a minimal effect on nonemergency vehicles 

traveling within the road network (See references Lu and Kim 2017; Jordan and Cetin 2014; ElDalil, Sharkas, and Khedr 2017; Xie et al. 2017; Viriyasitavat and Tonguz 2012). 

For example, Xie et al. (2017) studied the speed of emergency vehicle responses within a traffic 

simulation model of an intelligent transportation system (ITS) in which all vehicles and 

infrastructure were connected. Emergency vehicles transmitted information about their intended 

route and current lane via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) 

communication. The receiving infrastructure responded by turning stoplights in the path of the 

emergency vehicle green. Connected Vehicles (CVs) cleared a path for the emergency vehicle by 

changing lanes and yielding at intersections. Compared to a conventional transportation system 

in which V2V and V2I communication was not possible emergency, vehicles within this 

simulated ITS were able to reach their destinations more quickly. The benefit of CAVs was 

found across a variety of road configurations, travel path lengths, and traffic volumes. 

Research on interactions among CAVs and emergency vehicles has focused on highly automated 

driving systems (i.e., SAE Level 4 or Level 5) (SAE International 2021). The goal has been to 

generate and test algorithms that optimize the response of automated systems to emergency 

vehicle communications. Communication among connected emergency vehicles and drivers 

operating vehicles with lower levels of automation (i.e., Level 2 or Level 3) may also benefit 

from a TIM response. For example, responding to emergency vehicles is likely to be outside the 

operational domain of many Level 3 systems. Receiving intended path information from an 

emergency vehicle could allow a Level 3 system to initiate a takeover request more quickly, 

giving drivers more time to respond to the emergency vehicle and, thus, improving efficiency 

and safety. 
2 

Messages transmitted by connected emergency vehicles could be even more beneficial to drivers 

operating a Level 2 system (SAE International 2021). When using a Level 2 automated system, 

the driver is responsible for recognizing when it is safe to use the system and when the system 

should not be used. Unlike drivers operating a vehicle with a Level 3 system, drivers operating a 

Level 2 system cannot rely on the automated system to identify or alert them when an emergency 

vehicle is present. Therefore, V2V communication has the potential to increase the distance at 

which a driver can detect an approaching emergency vehicle (Xie et al. 2017). 

In most cases, emergency vehicles transmit information about their location via lights and sirens. 

The distance at which drivers detect these signals is impacted by many factors, including siren 

pitch, the direction from which the vehicle is approaching, the amount of background noise in 

the area, and the number of objects in the environment because trees and buildings can block 

sound (Maddern, Privopoulos, and Howard 2011). Detection distances are reduced by 

noise-cancelling vehicle insulation, radio use, and hearing loss (De Lorenzo and Eilers 1991).

Increasing siren volume is not a viable means of increasing emergency vehicle detection. Sirens 

with high volumes become a nuisance to communities and can cause hearing loss in emergency 

vehicle drivers, and unnecessary stress for patients during transport (Kupas 2017). According to 

Skeiber, Mason, and Potter (1978), “sirens will never become an effective warning device 

without also becoming an intolerable community noise problem. Order of magnitude 

improvements in future warning effectiveness will have to be based upon non-auditory means.” 

In-vehicle alerts issued by connected emergency vehicles have the potential to serve as a 

nonauditory means of notifying drivers of an emergency vehicle (Skeiber, Mason, and Potter 

1978). 

Connected emergency vehicles offer the potential to increase the distance at which drivers can 

detect emergency vehicles. If drivers can use this information to successfully clear a path for an 

emergency vehicle, the time required for responders to reach a traffic incident could be reduced. 

One factor that may impact driver response is CAV market penetration (MP). Studies on speed 

and response to traveler information have demonstrated that drivers are influenced by the actions 

of vehicles around them (Xuan and Kanafani 2014). Since the introduction of CAVs is expected 

to be gradual, some drivers may receive connected messages while driving among vehicles that 

have not received the same information. In other instances, a driver may be traveling among 

CAVs that are receiving information unknown to the driver. Understanding the impacts of 

different levels of CAV MP on drivers’ responses to emergency vehicles could help TIM 

responders prepare to operate in mixed fleets. 

This study explored the potential for V2V communication to speed drivers’ responses to 

emergency vehicles. The participants operating either a manual vehicle or a vehicle with Level 2 

automation drove in a semiurban environment. After a period of driving, the participants became 

the third vehicle in a four-vehicle string. An emergency vehicle approached the string from 

behind. Participant vehicle connectivity was manipulated so that half of the participants received 

an in-vehicle message indicating that the emergency vehicle was approaching. CAV MP was 

also manipulated. In the “no MP” condition, none of the surrounding vehicles received an alert 

about the approaching emergency vehicle. In the “low MP” condition, the vehicle directly in 

front of the participant vehicle received an alert and responded by pulling onto the shoulder of 

the roadway. In the “full MP” condition, all three vehicles in the string received and responded to 

the alert. The participants’ response time to the emergency vehicle were measured. Vehicle 
3 

kinematics and eye tracking were also measured to judge the impact of vehicle automation, V2V 

communication, and CAV MP of drivers’ responses to emergency vehicles. The goal of this 

research was to examine how the driver changes the vehicle’s speed/position in response to the 

emergency vehicle, investigate where the driver is looking after the emergency vehicle alert, and 

observe other safety metrics related to getting back on the road.

5 

CHAPTER 2. METHOD

PARTICIPANTS

Ninety-six drivers from the Washington, DC, metropolitan area participated in the study. 

Forty-eight males and 48 females participated, and an equal number of males and females under 

the age of 46 yr and 47 yr or older completed the study. 

APPARATUS

Simulator

The study was conducted using the Federal Highway Association’s (FHWA) highway driving 

simulator at Turner-Fairbank Highway Research Center. The highway driving simulator consists 

of a full automobile chassis surrounded by a semicircular projection screen (radius of 8.5 ft). 

Three high-definition projectors rendered a 200° view (motorists’ field of view) of high-fidelity, 

computer-generated roadway scenes. Three liquid-crystal display panels simulated the vehicle’s 

rearview mirror and side mirrors. The simulator had a 6-degree-of-freedom motion base that 

provided pitch and surge (for acceleration and braking), lateral, roll, yaw (for curve and turning 

forces), and heave (for bumps) cues in concert with the visual environment. The simulator's 

sound system provided engine, wind, tire noises, and other environmental sounds. 

Simulator Scenario

The participants drove on an 18-mi undivided two-lane road in a semiurban environment. The 

roadway was 12-ft wide and had a 3-ft shoulder. Light traffic in the opposing lane was included 

to deter the participants from crossing the lane boundary. The point at which the participants 

encountered the emergency vehicle was manipulated between subjects. Half the participants 

traveled for 11 mi before encountering drivers in their travel lane, and half the participants 

traveled for 2 mi. During this 11-mi drive, the participants passed additional elements of a 

semiurban road environment, including a marked bicycle lane with a single bicyclist, a transition 

to a shared bicycle lane, and a single bicyclist traveling on the side of the roadway in the shared 

bicycle lane. 

During the drive, the participants passed through a signalized intersection where a vehicle was

waiting on the cross street. When the participant’s vehicle cleared the intersection, the waiting 

vehicle turned onto the roadway the participant was traveling on so that it followed the 

participant’s vehicle. The trailing vehicle accelerated until there was a 1.1-s gap between the 

trailing vehicle and the participant’s vehicle, and the trailing vehicle maintained that gap. Two 

miles after passing the first intersection, the participant approached another signalized 

intersection where two vehicles were waiting at a red light. When the gap distance between the 

participant’s vehicle and the vehicle at the back of the queue reached 1.5 s, the light turned

green, and the two vehicles accelerated to 35 mph such that the participant vehicle became the 

third vehicle within a four-vehicle string. 

As depicted in figure 1, the vehicles traveled together for approximately 1 mi, at which point, a 

simulated emergency vehicle traveling at 45 mph appeared to approach 1,000 ft behind the 
6 

participant vehicle. When the emergency vehicle was within 1/10 of a mile from the participant 

vehicle, an auditory siren was sounded and increased in volume as the emergency vehicle 

approached the participant vehicle. The participants in the CV condition received an alert on the 

center console indicating that an emergency vehicle was approaching from behind (the 

participants without vehicle connectivity did not receive an alert). The alert (shown in figure 2) 

included an audio component and a written message displayed in the center console of the 

vehicle. One second after the alert was issued (or would have been issued), any simulated CVs 

within the string began pulling over to the right shoulder of the road. The participants in the full

and low MP conditions observed other vehicles in the string pulling over onto the shoulder of the 

roadway. 

• In the full MP condition, all three vehicles in the string pulled over. 

• In the low MP condition, only the vehicle directly in front of the participant vehicle 

pulled over, and the other two vehicles continued driving down the roadway. 

• In the no MP condition, none of the vehicles pulled over. 

Approximately 1 s after the simulated CVs pulled over, simulated vehicles within the string that 

were not connected (i.e., the simulated vehicles that had not already pulled over based on the 

in-vehicle alert), began pulling to the side of the roadway. Shortly afterward, the emergency 

vehicle appeared in the driver’s rearview mirror. The participant was free to decide when to pull 

over or whether to pull over. The emergency vehicle maintained a 2-ft gap while passing the 

participant’s vehicle and then exited the roadway at the nearest intersection approximately 1 mi 

downstream. After the emergency vehicle had passed, the remaining vehicles returned to the 

roadway and traveled to the next intersection where they also exited the roadway. The participant 

then continued to drive in the semiurban environment for one additional mile. 

Source: FHWA.

Figure 1. Illustration. Simulated scenario. 
7 

Source: FHWA. 

Figure 2. CV alert message. 

Eye-Tracking System

A fixed eye-tracking system was used to collect participant glance data. The system was 

composed of three fixed cameras mounted on the vehicle’s dashboard and focused on the 

participant’s eyes. The fixed system required the participant to wear additional sensors. Prior to 

use, the system was calibrated to each participant’s unique body dimensions.

EXPERIMENTAL DESIGN

The 96 participants were divided equally across 3 independent variables. Table 1 displays the 

number of participants for the three independent variables included in this study, all of which 

were manipulated between subjects. First, the level of vehicle automation was manipulated so 

that half the participants drove a manual vehicle and half drove a vehicle with Level 2 

automation (SAE International 2021). Second, vehicle connectivity was also manipulated. Half 

the participants drove a CV that received an in-vehicle alert, and the other half received no alert. 

Third, the level of CAV MP of the surrounding traffic was manipulated. In the no MP condition, 

none of the other three vehicles the participant was traveling with on the roadway received an 

advanced alert about the emergency vehicle. In the low MP condition, one of the three other 

vehicles received an advanced alert that the emergency vehicle was approaching and pulled over 

to the side of the road in response to the alert. In the full MP condition, all three vehicles 

received alerts about the approaching emergency vehicle and pulled over in response to the 

message. In the low MP condition and the full MP condition, the vehicles received the alerts 

before the emergency vehicle was visible to the driver. 

Table 1. Experimental design table.

Level of 

CAV MP

Manual 

Vehicle: 

CV Alerts

Manual 

Vehicle: 

No CV Alerts

Level 2 

Vehicle: 

CV Alerts

Level 2 

Vehicle: 

No CV Alerts Total 

None 8 8 8 8 32 

Low 8 8 8 8 32 

Full 8 8 8 8 32 

Total 24 24 24 24 96 
8 

PROCEDURE

The research team asked the participants to review and sign an informed consent. The 

participants had to show a valid driver’s license. The team used a Bailey-Lovie eye chart to 

verify a minimum of 6/12 (20/40) visual acuity, with correction if necessary (Bailey and Lovie 

2013). The participants provided a symptoms baseline by completing a simulator sickness 

questionnaire. 

Next, the participants received study instructions. All participants received a brief introduction to 

the concept of connected and automated vehicles. The participants assigned to a Level 2 

automation condition viewed a brief presentation describing the functions of the lateral and 

longitudinal systems present in their vehicles (SAE International 2021). 

The participants then completed a practice drive to become familiar with the simulator. Each 

practice drive lasted 3–5 min, with the exact time determined by how long it took an individual 

participant to become familiar and comfortable with the controls of the simulated vehicle. During 

the practice drive, the research team asked the participants to accelerate, brake, and change lanes.

The participants assigned to the Level 2 condition performed these tasks with the Level 2 system 

engaged (SAE International 2021). The participants in the CV condition received an alert during 

the practice drive. After completing the practice drive, the participants exited the vehicle and 

completed the simulator sickness questionnaire a second time. The participants then completed 

the simulated scenario and were paid before leaving. 

ANALYSIS

The goal of the analysis was to investigate the participants’ responses to an emergency vehicle

and any effects on driving safety. The study team assessed the participant’s responses with 

driving performance measures such as distance, speed, steering wheel variability, lane position, 

acceleration/deceleration, and following distance. The researchers used eye tracking to assess 

in-vehicle alerts as a source of distraction while driving. The researchers used appropriate 

statistical analysis to test the associations between variables for each dependent variable. These 

analyses were used to assess the impact of vehicle automation, V2V communication, and CAV

MP on drivers’ responses to emergency vehicles. A significance level of 0.05 was adopted.
9 

CHAPTER 3. RESULTS

The analysis investigated the participants’ responses to the emergency vehicle and any effects on 

driving safety. The study team assessed the participants’ behavior by examining what proportion 

of the participants responded to the emergency vehicle by pulling over, the participants’ response 

latency, and the participants’ eye gaze on center stack, mirrors, and roadway. The team examined 

the effects on driving safety of the participants’ distance to other vehicles at the time the 

emergency vehicle passed, the participants’ blinker usage, the number of vehicle incidents when 

the participant reentered the roadway, the participants’ driving speed, speed variability, 

following distance, and lane variability. 

RESPONSE TO EMERGENCY VEHICLE

Driver performance metrics were used to assess participant response to the emergency vehicle. 

The metrics investigated were as follows: Pullover behavior, response latency, and eye gaze. 

Pullover Behavior 

The researchers examined the proportion of the participants responding to the emergency vehicle 

by pulling over as a function of vehicle automation level, CV condition, MP, age, and gender. 

Seventy-six of the 96 study participants pulled over and 20 participants did not. Table 2 shows 

the number of participants who pulled over by vehicle automation type, CV type, and MP

conditions. Examining pullover rates by vehicle automation showed that for manual vehicle 

participants, 41 out of 48 (85 percent) pulled over versus 35 out of 48 (73 percent) of Level 2 

vehicle participants (SAE International 2021). Pullover rates by CV type showed that more 

participants with alerts pulled over (43 out of 48 (89 percent) compared to the participants 

without alerts (33 out of 48 (69 percent)). Pullover rates by MP revealed roughly equal 

proportions across full, low, and no MP. 

Table 2. Pullover behavior by vehicle automation type, CV type, and MP. 

Pulled 

Over?

Manual

Vehicle

Level 2

Vehicle CV Alert

No CV 

Alert Full MP Low MP No MP

Yes 41 35 43 33 26 26 24

No 7 13 5 15 6 6 8

Total 48 48 48 48 32 32 32

An exact logistic regression that examined the association between the pullover decision and the 

independent variables showed that the participants who received an alert about an approaching 

emergency vehicle were 3.5 times (p = .013) more likely to pull over than the participants who 

did not receive an alert. No significant evidence showed that vehicle automation and CAV MP

could affect the participants’ decisions to pull over. No statistical evidence of gender or age 

influence was found. 

The research team investigated how far the participants moved their vehicles toward the right 

edge to yield to the passing emergency vehicle. The team examined the distance between the 

center of the participant vehicle and the left edge of the right line marking the right lane. As 
10 

shown in table 3. Level 2 and manual vehicle participants who pulled over positioned their 

vehicles on average 3.5 and 3.1 ft from the right line (SAE International 2021). For manual 

vehicle participants who did not pull over, they positioned their vehicle an average of 3.1 ft from 

the right line at the time the emergency vehicle passed. The Level 2 automation participants who 

did not pull over were found to move over less and farther from the right line (4.9 ft) compared 

to the other participants. 

Table 3. Average pullover distance at time emergency vehicle passes. 

Pulled Over? Level 2 Vehicle (ft) Manual Vehicle (ft) 

Yes 3.5 3.1 

No 4.9 3.1 

Response Latency

The research team investigated how quickly the participants responded to an emergency vehicle. 

The team assessed the distance the participants traveled before reducing their vehicle speeds to 

20 mph after the emergency vehicle had appeared. Figure 3 shows the mean distances traveled as 

a function of MP, CV condition, and vehicle automation. Across the three MP levels, higher 

levels of MP tended to result in less distance to reduce speeds. Within MP levels, the participants 

receiving CV alerts tended to reduce their speeds sooner (i.e., travel less distance) than the 

participants not receiving alerts. 

Linear regression showed a significant interaction between MP, CV condition, and vehicle 

automation (p = 0.022). At full MP, the participants in Level 2 vehicles with CV alerts reduced 

speeds sooner (and traveled about 4132 ft less) than the participants in Level 2 vehicles without 

CV alerts (SAE International 2021). However, the participants in Level 2 vehicles with CV alerts 

reduced speeds later (and traveled about 61 ft farther) than those driving manual vehicles with 

CV alerts. When comparing the participants driving Level 2 vehicles with CV alerts, those who 

were in full MP reduced speeds much sooner (on average traveled about 678.5 ft less) than those 

in no MP. When comparing the participants driving Level 2 vehicles with CV alerts in full

versus low MP, the participants who were in a full MP fleet reduced speeds sooner (on average 

about 95 ft less) in distance than those in low MP. 
11 

Source: FHWA. 

Figure 3. Graph. Mean distance to reduce speed by MP, connectivity, and vehicle

automation. 

Eye Gaze on the Console

The researchers classified the participants’ eye gaze as either looking out the front windshield 

(i.e., center projection), side windows (i.e., left/right screen), at locations inside the vehicle (e.g., 

center stack console), or at the left/right mirrors. To assess a participant’s behavior monitoring 

the emergency vehicle, the research team examined the proportion of time the participant spent 

on the center stack console. The proportion of time was during the period between when the 

emergency vehicle appeared and when the emergency vehicle passed the participant. During this 

period, 54 participants looked at the center stack console, and 42 participants did not look at it. 

Figure 4 displays the frequency of the participants by proportion of time as a function of MP, CV 

condition, and vehicle automation. In general, most of the participants (30 out of 54 (56 percent)) 

tended to have shorter gaze times (less than 1 percent) as depicted in the solid black bars. The 

frequency for longer gaze times tended to decrease with longer times. 
12 

Examination of gaze times in terms of MP, connectivity, and vehicle automation found the 

following: 

• The frequency of the participants were relatively evenly divided between the 0, 0.25, and 

1 MP levels (19, 17, and 18, respectively). 

• Most of the participants who gazed at the center console also had CV alerts (40 out of 

54). 

• The frequency was slightly higher for the participants with manual versus Level 2 

vehicles (30 versus 24 participants). 

Source: FHWA. 

Figure 4. Graph. Frequency and proportion of time of console eye gazes by MP, 

connectivity, and vehicle automation. 

Eye Gaze on the Road

The research team also examined the proportion of time the participants spent looking at the 

roadway (left/right screen and center projection) during the period between when the emergency 

vehicle appeared and when the emergency vehicle passed the participant. Figure 5 displays the 

frequency of proportion of a participant’s eye gaze on the roadway as a function of MP, CV 

condition, and vehicle automation. Regardless of MP level, the participants with CV alerts 

tended to spend slightly less time gazing at the roadway. A Poisson regression showed that the 

participants with CV alerts had about 7 percent (p = 0.0001) reduction in the time they spent 

looking at the roadway compared to the participants whose vehicles did not have CV alerts

(Frost 2012). No other effects were found to be statistically significant. 
13 

Source: FHWA. 

Figure 5. Graph. Proportion of road eye gazes by MP, connectivity, and vehicle 

automation. 

Eye Gaze on Rearview Mirror and Side Mirrors

The research team examined the proportion of time the participants spent looking at the rearview 

mirror and side mirrors during four intervals as a function of MP, connectivity, and vehicle type. 

The four intervals were: time from an emergency vehicle appearing (1,000 ft behind the 

participant’s vehicle) to the alert; from alert to siren; from siren to the emergency vehicle in 

sight; and from the emergency vehicle in sight to the emergency vehicle passing. Figure 6 shows 

intervals before the alert and between the provided alert to when the siren sounded. Figure 7

shows intervals between the siren sounding to the emergency vehicle in sight and between the 

emergency vehicle in sight to the emergency vehicle passing. Figure 6 shows that prior to the 

alerts, the participants spent 2 percent or less time gazing at the mirrors regardless of MP, 

connectivity, or vehicle type. During the interval between alert to siren on, only the participants 

in the no MP condition that had CV alerts increased their eye gazes to more than 5 percent. 

Figure 7 shows that during the intervals after the siren was on, all participants tended to increase 

their gazes on the mirrors compared to before the siren was heard by the participants. During the 

interval between emergency vehicles in sight and until the emergency vehicle passed, Level 2 

participants generally used the mirrors more than the manual participants in similar MP and 

connectivity conditions. 
14 

Source: FHWA.

Figure 6. Graph. Proportion of mirror eye gaze before siren. 

Source: FHWA.

Figure 7. Graph. Proportion of mirror eye gaze after siren. 
15 

EFFECTS ON DRIVING SAFETY 

Distance to Other Vehicles

The research team examined the mean distance between the participant vehicle and the other 

three vehicles at the time the emergency vehicle passed. Figure 8 displays the mean distances by 

MP, CV condition, and vehicle automation. In the no MP level condition, the average distance to 

other vehicles was relatively similar, and ranged from 469 to 568 ft regardless of connectivity 

and vehicle automation. In the low MP level condition, the average distance to other vehicles 

showed greater disparity between those vehicles with and without CV alerts. The participants 

with CV alerts ranged from 420 to 489 ft versus 673 to 686 ft for the participants without CV 

alerts. In the full MP level condition, the average distance to other vehicles showed the greatest

disparity between those vehicles with and without CV alerts. The participants with CV alerts 

ranged from 141 to 240 ft versus 538 to 656 ft for the participants without CV alerts. A 

significant interaction between CV and MP (p < 0.0001) was found, indicating that the 

participants with CV alerts in low MP and full MP were significantly closer to the other vehicles 

at the time the emergency vehicle passed compared to those in no MP. 

Linear regression showed that the participants driving Level 2 vehicles were significantly farther 

away (16 m, p = 0.02) from the other vehicles at the time of emergency vehicle passed compared 

to those driving manual vehicles. 

Source: FHWA. 

Figure 8. Graph. Mean distance between vehicles. 
16 

Blinker Use

The research team recorded the participants’ use of right or left blinkers at the time the 

participants yielded to an emergency vehicle and when the participants returned to the roadway. 

As shown in table 4, male drivers used the blinkers more than female drivers (32 out of 48 male 

drivers used the right blinkers versus 21 out of 48 female drivers at the time the participant 

yielded to the emergency vehicle; 26 out of 48 male drivers used the left blinkers versus 14 out 

of 48 female drivers when returning to the roadway). 

No significant association between the use of blinkers and the independent variables was found. 

Male drivers were found to be 2.5 times (p = 0.04) more likely to use the right blinker and 

2.8 times (p = 0.02) more likely to use the left blinker than female drivers. 

Table 4. Blinker use by gender when yielding to the emergency vehicle and returning to 

roadway. 

Gender

Used Right 

Blinker to 

Yield

Did Not Use 

Right Blinker to 

Yield

Used Left Blinker 

to Return to 

Roadway

Did Not Use Left 

Blinker to Return to 

Roadway

Male 32 16 26 22

Female 21 27 14 34

Vehicle Incidents 

The research team observed no vehicle crashes when drivers reentered the roadway shortly after 

the emergency vehicle had passed. 

Driving Speed and Variability

The participants traveled down an undivided two-lane road in a semiurban environment with a 

35-mph speed limit. The participants were instructed to drive as they normally would on the 

roadway. The research team examined speeds to investigate if any of the independent variables 

affected driving speeds. Figure 9 shows the mean speeds during the interval from emergency 

vehicle appearance to emergency vehicle passing the participant as a function of MP, CV 

condition, and vehicle automation. Participant speeds in the no MP condition (25.7–30.8 mph) 

tended to be faster than participant speeds in the low MP condition (15.4–27.6 mph) and full MP 

condition (14.3–24.3 mph). 

Linear regression showed significant interaction between CV and MP. The participants with CV

alerts in low MP had, on average, a lower speed (6.9 mph slower, p < 0.0001) than those in a no 

MP fleet. The participants with CV alerts in full MP had a lower speed (4.6 mph slower, p = 

0.003) than those in a no MP fleet. Evidence showed no significant effect of vehicle automation 

on driving speed. The significant effects of gender and age were not found. 
17 

Source: FHWA. 

Figure 9. Graph. Mean speeds by MP, connectivity, and vehicle automation. 

The research team investigated speed variability to identify if MP, CV condition, and vehicle 

automation had any effect on speed variability. Figure 10 shows the speed variability as a 

function of MP, CV condition, and vehicle automation. Within each MP level, the participants 

with CV alerts tended to have greater speed variability than the participants without CV alerts. 

A robust linear regression showed significant interaction between CV and MP. The participants 

with CV in low MP had, on average, a greater speed variability (3.3 mph greater, p = 0.0003) 

than those in a no MP fleet. The participants with CV alerts in full MP had a greater speed 

variability (2.7 mph greater, p = 0.003) than those in a no MP fleet. Evidence showed no 

significant effect of vehicle automation on variability of driving speed. The significant effects of 

gender and age were not found.

 
18 

Source: FHWA. 

Figure 10. Graph. Speed variability by MP, connectivity, and vehicle automation. 

Following Distance

The participants drove as the third vehicle in a four-vehicle string when the emergency vehicle

appeared. The research team examined the participants’ average following distance to assess if 

any of the independent variables affected following distance. Figure 11 shows the average 

following distance as a function of MP, CV condition, and vehicle automation. Across all MP 

levels, the participants with CV alerts and manual vehicles tended to have shorter following 

distances compared to the participants with CV alerts and Level 2 vehicles.

A robust linear regression showed an interaction between CV and MP as well as vehicle 

automation and MP was found to be statistically significant (both p < 0.05). In low MP, the 

participants with CV alerts on average had 151 ft shorter following distance compared to those 

without CV alerts. In full MP, the participants driving Level 2 vehicles on average had 98.4 ft 

shorter following distance compared to those who driving manual vehicles. No effect of gender 

and age was found. 
19 

Source: FHWA. 

Figure 11. Graph. Mean following distance by MP, connectivity, and vehicle automation. 

Lane Variability

Figure 12 shows the average lane variability by MP, connectivity, and vehicle automation. The 

participants driving in no MP conditions had slightly less lane variability compared to 

participants driving in low or full MP conditions. In the no MP conditions, the participants’ 

driving Level 2 vehicles had slightly less lane variability than those driving manual vehicles. 

This result was especially apparent for the participants without CV alerts (1.24 ft versus 1.90 ft). 

In the low MP conditions, the participants driving Level 2 vehicles again had slightly less lane 

variability than those driving manual vehicles but only when the participants did not have CV 

alerts. In the full MP condition, the lane variability for both connectivity and vehicle conditions 

were in the upper range (2.53–2.76 ft) compared to the no and low MP levels. 

A robust linear regression showed that the participants driving Level 2 vehicles had slightly 

lower (0.23 ft, p = 0.03) lane variability compared to those driving manual vehicles. The 

participants in the low and full MP conditions had slightly more lane variability

(0.43 ft, p = 0.0006 and 0.75 ft, p < 0.0001 , respectively) compared to those in the no MP

conditions. A significant interaction was not found between MP, connectivity, and vehicle 

automation. No significant interaction was found in age and gender effects. 
20 

Source: FHWA. 

Figure 12. Graph. Mean lane variability as a function of MP, connectivity, and vehicle 

automation.
21 

CHAPTER 4. DISCUSSION

Connected emergency vehicles communicating to CVs offer the potential to increase the distance 

at which drivers can detect emergency vehicles (Xie et al. 2017). This study found that CV alerts 

were effective in getting the participants to yield to an approaching emergency vehicle. Driving 

with CV alerts led to increased pullover rates and reduced speeds to pull over sooner than the 

participants without CV alerts. The participants driving Level 2 vehicles with CV alerts in low or 

full MP reduced speeds much sooner than those vehicles with no MP (SAE International 2021). 

These benefits were greatest in the full MP condition. However, in low or full MP, the 

participants in Level 2 vehicles with CV alerts reduced speeds slightly later than the participants 

with manual vehicles and CV alerts. Eye-tracking analyses found that the participants with CV 

alerts tended to spend more time gazing at the console and less time gazing at the roadway 

during the period from when the emergency vehicle appeared to when the emergency vehicle

passed. No crashes occurred at any time from when the emergency vehicle appeared to when 

vehicles reentered the roadway. Eye gaze on the mirrors increased when the emergency vehicle

was in sight until the emergency vehicle passed, with the Level 2 participants generally using the 

mirrors more than the manual participants in similar MP and connectivity conditions. 

Traveler information studies have also demonstrated that drivers are influenced by the actions of 

vehicles around them (Xuan and Kanafani 2014). The research team had similar findings in this 

study, as the participants in the low and full MP reduced their speeds and pulled over sooner than 

the participants in no MP. Also, the level of MP affected the rate at which Level 2 drivers 

reduced speeds. 

In terms of vehicle safety, the participants driving Level 2 vehicles had greater separation from 

other vehicles at the time the emergency vehicle passed compared to those driving manual 

vehicles. In general, the participants with CV alerts and low or full MP had lower speeds, greater 

speed variability, shorter following distances, and greater lane variability compared to those 

vehicles with no MP. Overall, the results and findings from this study support a promising future 

of V2V communication for emergency vehicle awareness and response and the potential of 

CAVs to help responders arrive at traffic incidents more quickly. The participants receiving the 

CV alerts were more likely to pull over, use the information to pull over sooner, and reduce their 

vehicle speeds sooner. The benefits of full MP CV communication for emergency vehicle

warnings appear to exist even at lower MP levels. Future research opportunities could also 

examine driver safety metrics (e.g., vehicle separation) after the emergency vehicle has passed to 

investigate possible impacts of vehicle automation, connectivity, and MP as vehicles reenter the 

roadway.

23 

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HRSO-30/02-24(WEB)E Recycled

Recyclable

Recommended citation: Federal Highway Administration,

Response to Emergency Vehicles When Driving in a Mixed Vehicle Fleet 

(Washington, DC: 2024) https://doi.org/10.21949/1521733