Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-22-085 SEPTEMBER 2022

Ensuring Cooperative Driving 

Automation (CDA) and Vulnerable 

Road Users (VRUs) Safety 

Through Infrastructure
FOREWORD

Vulnerable road users (VRUs), including pedestrians, bicyclists, motorcyclists, and various 

micromobility users, account for a significant share of roadway fatalities. VRUs are at the most 

risk in urban areas, and, thus, urban areas are a focus area for implementing new transportation 

technologies. These new technologies include automated driving systems (ADS) and cooperative 

driving automation (CDA), which can potentially have positive or negative effects on VRU 

safety.

This literature review documents the potential impact of ADS-equipped vehicles and CDA 

technology on VRU safety and the potential role of infrastructure in facilitating safe interactions. 

This review discusses the factors that currently influence VRU collision rates, especially in 

high-risk urban areas. It also examines the capabilities and needs of ADS-equipped vehicles 

when interacting with VRUs and the additional positive and negative impacts that the 

deployment of CDA technologies are likely to have on VRU safety. Finally, the review includes 

a prioritized list of research gaps and safety risks that were identified by a panel of ADS and 

VRU experts who met to discuss the potential challenges associated with ADS-VRU 

interactions.

This report should be of interest to transportation engineers and researchers, State and local 

transportation agencies, and others who have an interest in VRU safety, ADS-equipped vehicles 

and CDA technologies, and the role of infrastructure in facilitating VRU safety.

Brian P. Cronin, P.E. 

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. 

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

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

objective of the document. 

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. 

Recommended citation: Federal Highway Administration, Ensuring Cooperative Driving Automation 

(CDA) and Vulnerable Road Users (VRUs) Safety Through Infrastructure (Washington, DC: 2022) 

https://doi.org/10.21949/1521914. 
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-22-085

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

4. Title and Subtitle

Ensuring Cooperative Driving Automation (CDA) and 

Vulnerable Road Users (VRUs) Safety Through Infrastructure

5. Report Date

August 2022

6. Performing Organization Code:

HRSO-30

7. Author(s)

Starla Weaver (ORCID: 0000-0002-9559-8337) Jose Calvo

(ORCID: 0000-0001-8820-702X), Ananna Ahmed (ORCID: 

0000-0002-5353-508X), Jesse Eisert (ORCID: 0000-0003-

0116-5141) 

8. Performing Organization Report No.

9. Performing Organization Name and Address

Leidos, Inc

6300 Georgetown Pike

McLean, VA 22101

10. Work Unit No.

11. Contract or Grant No.

693JJ319D000012

12. Sponsoring Agency Name and Address

Office of Safety and Operations

Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period Covered

Final Report; May 2021–July 2022

14. Sponsoring Agency Code

ITS/JPO and HRSO-30

15. Supplementary Notes

The contracting officer’s representative was Brian Philips (ORCID 0000-0002-8426-0867). 

16. Abstract

Vulnerable road users (VRUs), including pedestrians, bicyclists, motorcyclists, and a variety of micromobility 

users, are at an increased risk for collisions, severe injuries, and fatalities relative to other road users, particularly 

in crowded urban environments. New transportation technologies could have both positive and negative effects on 

VRU safety. These new technologies include automated driving systems (ADS), which are capable of controlling 

vehicles with no or limited input from human drivers and cooperative driving automation (CDA), which send and 

receive cooperative and safety messages. The current literature review assesses the potential impact of 

ADS-equipped vehicles and CDA technology on VRU safety and the potential role of infrastructure in facilitating 

safe interactions. The review also includes a prioritized list of issues related to human factors and generated 

research needs, based on feedback from a panel of subject matter experts.

17. Key Words

Vulnerable road users, automated driving systems, 

cooperative driving automation, infrastructure

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

32 

22. Price

N/A

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

TABLE OF CONTENTS

INTRODUCTION......................................................................................................................... 1

VRU Risk Factors ......................................................................................................................... 2

Speed ....................................................................................................................................... 2

Visibility.................................................................................................................................. 2

Location and Infrastructure ................................................................................................. 2

VRU Characteristics and Behavior ..................................................................................... 4

ADS................................................................................................................................................. 5

Recognize VRUs..................................................................................................................... 7

Yield Based on Local Laws ................................................................................................... 8

Communicate Intent.............................................................................................................. 9

Predict VRU Intent.............................................................................................................. 11

Safe Driving Patterns .......................................................................................................... 12

CDA .............................................................................................................................................. 13

Expert Opinions .......................................................................................................................... 16

Prioritized List of Research Needs ............................................................................................ 18

Areas of Concern ................................................................................................................. 18

Potential Solutions ............................................................................................................... 20

REFERENCES ............................................................................................................................ 21
iv

LIST OF FIGURES

Figure 1. Levels of Automation (SAE International On-Road Automated Vehicle 

Standards Committee, 2018). ................................................................................................ 6

LIST OF ABBREVIATIONS

AADT annual average daily traffic

ADS automated driving systems 

AV autonomous vehicle 

CDA cooperative driving automation 

eHMI external human-machine interface

FHWA Federal Highway Administration

GPS Global Positioning System

LiDAR light detecting and ranging 

MUTCD Manual on Uniform Traffic Control Devices

NHTSA National Highway Traffic Safety Administration

OEMs original equipment manufacturers

SME subject matter expert

TCDs traffic control devices 

VRU vulnerable road user 

V2I vehicle-to-infrastructure 

V2P vehicle-to-pedestrian

V2V vehicle-to-vehicle

X2X machine-to-machine 
1 

INTRODUCTION

Vulnerable road users (VRUs) account for a significant share of roadway fatalities. The term 

VRU refers to any road user who is not protected by the shield of a vehicle cab. This group 

includes pedestrians, bicyclists, motorcyclists, and a variety of micromobility users. In 2019, 

VRUs accounted for 34 percent (11,303) of all fatalities that occurred during a motor vehicle 

collision (National Center for Statistics and Analysis, 2020). Although VRU fatality rates have 

decreased from 2018 to 2019, VRUs are still tied with passenger car occupants for comprising 

the largest portion of fatalities from motor vehicle collisions and for making up a greater 

percentage of fatalities than the occupants of light trucks, large trucks, buses, and other vehicles. 

VRUs are at particular risk in urban areas. Since 2015, shifts in population density have led to 

overall fatality rates in urban areas becoming more prevalent than fatality rates in rural areas, 

with more than 58 percent (19,595) of all fatal crashes occurring in urban areas in 2019 (National 

Highway Traffic Safety Administration (NHTSA), 2020). This shift has had a particularly 

negative effect on VRU safety. Fatalities of all types of VRUs have increased in urban areas 

since 2010, with motorcyclist fatality rates up 36 percent, pedalcyclists up 49 percent, and

pedestrians up 62 percent in 2019 (NHTSA, 2020). 

Urban environments are likely to be the target area for the implementation of new transportation 

technologies. Vehicles equipped with automated driving systems (ADS) or driving systems 

capable of controlling vehicles with no or limited input from human drivers are currently 

undergoing testing (SAE International On-Road Automated Vehicle Standards Committee, 

2018). Likewise, cooperative driving automation (CDA), or components of the transportation 

system that are able to send and receive cooperative and safety messages, are in development 

(SAE International On-Road Automated Vehicle Standards Committee, 2020). Due to the 

presence of established infrastructure and larger potential customer bases, business models and 

market research suggest that many of the early adopters of ADS-equipped vehicles and CDA 

technology, such as rideshare and delivery companies, are likely to begin operations within 

urban environments (Legêne et al., 2020). The initial use of driving automation in crowded urban 

environments, where VRU traffic is also typically at its peak, indicates that interactions between 

ADS-equipped and CDA vehicles and VRUs will be common. Ensuring the safety of VRUs 

during these interactions might require the implementation of infrastructure solutions, 

countermeasures, and strategies. 

The goal of this current literature review is to assess the potential impact of ADS-equipped 

vehicles and CDA technology on VRU safety and determine the potential role of infrastructure in 

facilitating safe interactions. First, this review discusses the factors that currently influence VRU 

collision rates, especially in high-risk urban areas. Next, the review examines the capabilities and 

needs of ADS-equipped vehicles when interacting with VRUs. This examination is followed by a 

discussion of the additional positive and negative impacts that the deployment of CDA 

technologies is likely to have on VRU safety. Finally, the review ends with a discussion of 

research gaps and safety risks that were identified by a panel of ADS and VRU experts, who met 

to discuss the potential challenges associated with ADS-VRU interactions.
2 

VRU RISK FACTORS

Previous research has identified several major factors that increase VRU risks. These factors 

increase the VRUs’ risk of collisions and the potential for severe injuries and fatalities. Major 

risk factors include vehicle speed, time of day, location of road crossings and surrounding 

infrastructure, and VRU characteristics and behaviors. 

Speed

The speed of an oncoming vehicle is one of the known factors that increases VRUs’ injury risk. 

Injury and fatality rates from a crash involving VRUs are highly influenced by the speed of the 

vehicle involved. It has been well established in the literature that the risk of serious injury and 

death increases as speed increases (Rosén et al., 2011; Tefft 2013; Gårder 2004). This increase is 

more pronounced at speeds greater than 37 mph. Tefft (2013) found that for pedestrians, the risk 

of severe injury or death increases exponentially as speed increases, particularly when a 

pedestrian encounters a vehicle traveling at a speed greater than 40 mph. In addition to 

increasing the risk of serious injury and death, high speeds increase the probability of crashes, 

because vehicles traveling at high speeds have less time to stop. 

Due to the impact of speed on both crash risk and crash severity, several infrastructure 

countermeasures have been used to attempt to reduce speed in areas where VRUs are at risk. 

Lowering speed limits in areas with high volumes of pedestrian traffic can reduce vehicle speeds 

(Hu & Cicchino, 2020). In addition, installing roundabouts and multiway stop signs reduce 

drivers’ speed (Retting et al., 2003). Traffic-calming measures—including narrowing a road 

lane, adjusting the curvature of roadways, and adding pedestrian islands and speed humps—also 

slow down traffic, although research on whether the slower speeds generated by traffic-calming 

measures translate to increased pedestrian safety is mixed (Retting et al., 2003). 

Visibility

Visibility plays a major role in pedestrian safety. Most pedestrian fatalities (76 percent) occur 

after nightfall (NHTSA, 2019). Drivers at night have reduced sight distance in comparison to 

their daytime sight, and both pedestrians and drivers are more likely to be drowsy or impaired 

when traveling at night (Toran Pour et al., 2017). Dark roads are one of the leading causes of

VRU deaths because drivers on dark roads are not able to see VRUs, and VRUs often 

overestimate their visibility (Wang & Cicchino, 2020). Infrastructure that increases pedestrian 

visibility can improve road safety. This improvement can be achieved by increasing the number 

of roadway lightings near pedestrian crossings or by installing lights within crosswalks (Miller et 

al., 2004). During both nighttime and daytime hours, cars parked along the edge of roadways can

prevent drivers from seeing pedestrians who intend to cross the road. Eliminating roadside 

parking or creating diagonal parking can also help increase pedestrian visibility

(Retting et al., 2003).

Location and Infrastructure

Another factor that increases the risk of collisions involving pedestrians is the location at which 

such crashes occur and the infrastructure at the crash location. Traffic control devices (TCDs) are 

known to reduce the crash risk for VRUs (Gårder, 2004). Infrastructure can be used to separate 
3 

pedestrians from traffic, either temporally or spatially. Temporal separation can be achieved by 

implementing measures that prevent pedestrians and drivers from attempting to cross an 

intersection at the same time. For example, exclusive traffic signal phasing can be used to allow 

a time during which only pedestrians are able to cross the road. Exclusive traffic signal phasing 

leads to significant reductions in traffic crashes involving pedestrians. Pedestrians can also be 

temporally separated from drivers by long yellow lights that allow traffic to clear the intersection 

before pedestrians begin crossing. Pedestrian crossings that automatically detect and adjust their 

timings when a pedestrian is present on the roadway achieve the same result. Spatial separation 

of pedestrians from drivers can be achieved by overpasses and underpasses, provided that 

pedestrians consider such passageways to be safe and convenient. Sidewalks can keep 

pedestrians away from the edges of a road, whereas refuge islands and curb extensions can make 

crossing the road easier by segmenting large crossings into multiple, more manageable segments

(Retting et al., 2003). 

Road types also influence pedestrian crash risk. Wide roads, such as arterial roads and major 

collectors, have been shown to have higher crash rates than two-lane roads, even after accounting 

for differences in pedestrian volume (Gårder, 2004). Arterial roads are considered high-capacity

urban roads, with speeds and traffic flow just below freeways or motorways on the road 

classification hierarchy. Pedestrians, specifically, are at the highest risk of being involved in 

crashes on roadways that are in areas that have high traffic volume, are highly populated, and 

have employment density as well as areas with large concentrations of commercial or retail and 

multifamily residential land uses (Loukaitou-Sideris et al., 2007). Built environment and traffic 

density also make some roads more dangerous than other roads for pedestrians. A study by Zhao 

and Chen (2016) investigated data extracted from the Transportation Police Bureau in

Zhengzhou and Xi’an, China, and found several variables related to roadway type that were 

linked to bicyclists’ crash frequency. These variables included the number of left-turn lanes, 

number of through lanes, left turn annual average daily traffic (AADT), and annual average ratio 

or major direction AADT to minor direction AADT. As each of these variables increased, the 

crash frequency increased as well. The availability of midblock crosswalks was associated with a 

reduction in crash frequency. In addition, greater corner radius and greater shoulder width are 

associated with a higher number of pedestrian crashes (Fitzpatrick et al., 2016).

Most collisions involving pedestrians occur at major intersections (Rothman et al., 2012). Such 

collisions likely occur because most pedestrians cross the road at major intersections. 

Furthermore, major intersections typically contain large volumes of vehicle traffic moving at 

relatively fast speeds. Thus, both pedestrian exposure and crash rates may be overrepresented, in

particular, for intersections. Midblock crossings, especially those at unsignalized crossings and 

those at crossings with no markings, are the riskiest in terms of injury and death rates, especially 

for children (Rothman et al., 2012). 

In addition to the structure of the roadway and location of the crossing, the existence of markings 

and infrastructure that support safe crossings for pedestrians also contribute to the risk of crashes 

involving VRUs. Crosswalk markings are associated with slower speeds, both with and without a 

pedestrian present (Knoblauch & Raymond, 2000; Schneider et al., 2004). Crosswalk markings 

can increase the conspicuity of pedestrians and channel pedestrians who are crossing from

multiple midblock locations to a single, designated path. However, the effect that crosswalk 

markings alone have on pedestrian safety is unclear. Some reports indicate that these crossings 
4 

enhance safety (Knoblauch et al., 1988), whereas other researchers report that the markings are 

associated with an increased crash risk, particularly when the crossings are implemented with no 

other interventions on multilane roads (Herms, 1972; Koepsell et al., 2002; Zegeer et al., 2001). 

One possible reason for the increase in pedestrian crash risk on marked crosswalks could be a 

bias in the proportion of pedestrians crossing at those locations. Research shows that pedestrians 

typically prefer crossing at intersections with crosswalk markings (approximately 70 percent of 

pedestrians cross at marked intersections) (Zegeer et al., 2001). If crosswalk markings are added 

as a treatment to roadways that already have a high rate of pedestrian crashes, then the markings 

may actually encourage pedestrians to continue crossing at those high-risk locations. Thus, when 

installed at high-risk locations, markings are best used in combination with other interventions, 

such as curb extensions, a raised median, a crosswalk island, or pedestrian signals 

(Zegeer et al., 2001). 

Meir, Parmet, and Oron-Gilad (2013) provide another potential explanation for the mixed results 

with regard to the effectiveness of crosswalk markings at midblock locations: They found that 

under conditions of uncertainty, such as when pedestrians’ field of view was restricted by parked 

cars or road curvature, the presence of a marked crossing increased the tendency for pedestrians

to enter the roadway. The effect was especially exaggerated among children. Marked crossings 

might increase the confidence of pedestrians, such that they are more likely to attempt potentially

risky road crossings. The results suggest that unsignalized midblock crosswalk markings should 

not be used on roadways where pedestrians’ view of oncoming cars is restricted or in locations 

where children make frequent, unattended crossings. 

VRU Characteristics and Behavior 

The frequency of pedestrian crashes and fatalities varies as a function of age, with children 

and older adults being most at risk of severe injury or death (NHTSA, 2019). Because of their 

size, children tend to be less visible to drivers. Children also have underdeveloped perceptual 

motor coordination (Chihak et al., 2010), and they are less able to identify hazardous situations 

or judge roadways as dangerous than adults (Meir, Parmet, & Oron-Gilad, 2013; Meir, 

Oron-Gilad, & Parmet, 2015). As a result, children are more likely to quickly enter potentially 

dangerous roadways.

Older adults are also at an increased risk of being in a road-crossing accident

(Rothman et al., 2012). Age-related reductions in perceptual, motor, and cognitive abilities 

can place older adults at greater risk when they are attempting to cross a road. For example, 

as adults age, their useful field of view, or the area over which they can acquire information 

in a brief glance, narrows such that they may be less able to see oncoming traffic (Bromberg 

et al., 2012). Aging can also be associated with reductions in mobility that reduce walking 

speed (Lobjois et al., 2013). When faced with simple one-way road crossings, older adults are 

often able to compensate for reduced perceptual and motor functions by choosing larger gap 

sizes, such that their road-crossing ability is not impaired (Lobjos et al., 2003). However, when 

faced with multilane crossings, older adults tend to be less successful at adequately attending 

to and basing crossing decisions on traffic in the far lane than younger adults. This tendency 

puts older adults at a greater risk for injury or death when crossing multilane roadways 

(Dommes et al., 2014). When involved in a collision, older adults, whose health statuses and 
5 

bodies tend to be more fragile than younger adults, are at a much greater risk of severe injury or 

death (Hakamies-Blomqvist et al., 2004). 

Gender also influences VRU risk. Research suggests that males tend to engage in more 

risk-taking activities than females. This increased risk-taking has interesting implications on 

road-crossing abilities that vary by age. Specifically, assessments of children’s road-crossing 

ability demonstrate that male children tend to display more mature road-crossing behavior, or 

behavior that is more similar to that seen among adults, than their female counterparts (Shen et 

al., 2015). However, by the time males reach adolescence, this reduced fear and increased 

risk-taking behavior becomes a liability. For example, Schwebel et al. (2009) found that 

19-year-old males spent less time attending to traffic before entering a virtual roadway than their 

female counterparts, and as a result, young men are more likely to be involved in a collision than 

women of a similar age. Among adults overall, male pedestrian fatality rates are more than 

double the rates for females (NHTSA, 2019). 

VRU behavior is also concerning when assessing the risk to VRUs. Research shows that 

pedestrian distractions, especially distractions attributable to the use of handheld devices such as 

smartphones, create an increased risk for VRUs (Fitzpatrick et al., 2016). In addition to looking 

at or using cellphones, behaviors such as listening to music with headphones and talking with 

other VRUs are also distracting. On average, just under 50 percent of pedestrians at cross 

intersections engage in distracting behaviors, of which cellphone use makes up about 10 percent 

of those behaviors (Fitzpatrick et al., 2016). This distracted behavior is concerning because some 

drivers might be expecting some type of communication with a pedestrian based on their 

previous experiences when cellphone use was not as ubiquitous. Previous research indicates that 

pedestrians often (in roughly 90 percent of cases) attempt to communicate with drivers of 

oncoming vehicles to let the drivers know of their intention to cross (Rasouli et al., 2017). 

Rasouli et al. (2017) found that looking in the direction of an approaching vehicle was the most 

prominent form of communication and that for roughly 15 percent of the time, other gestures 

such as head nodding or hand waving were used. Given this finding, the high rate of distraction 

among pedestrians is concerning. If drivers have become reliant on these forms of 

communication, then pedestrian distraction could be a risk factor for VRUs’ interaction with 

conventional drivers. However, pedestrian-driver interaction is likely to be less of an issue as 

technology advances and ADS become more prevalent, provided ADS are not anticipating VRU 

communication. 

ADS

The development of driver support features and vehicle automation capabilities has the potential 

to change the way that VRUs and vehicles interact. The following section defines ADS 

capabilities, examines five features that ADS-equipped vehicles might need to interact safely 

with VRUs, and outlines the potential role infrastructure can play in facilitating that interaction.

SAE International On-Road Automate Vehicle Standing Committee (2018) specifies six levels of 

driving automation, based on the roles and responsibilities of the driver and the automated 

system (see figure 1).
6 

© 2020 SAE International. The summary table may be freely copied and distributed provided SAE International and 

J3016 are acknowledged as the source and must be reproduced AS-IS.

Figure 1. Levels of Automation (SAE International On-Road Automated Vehicle Standards 

Committee, 2018). 

Level 0, which is the lowest level, represents the conventional vehicle. A Level 0 vehicle may be 

equipped with advanced safety features that warn the driver about potential safety threats or 

implement momentary safety maneuvers (e.g., emergency braking, electronic stability control), 

but the driving task is not automated. 

When operating at Levels 1 and 2, the driver temporarily receives assistance from driver support 

systems that provide either lateral or longitudinal control (Level 1) or both lateral and 

longitudinal control functions (Level 2). The system is described as an assistive rather than an 

automated system because the driving task remains the responsibility of the driver. 

When operating at Level 3 or higher, the vehicle is controlled by the ADS, which is able to 

perform all vehicle operations within a specified operational design domain. Unlike at lower 

levels, where the driver is responsible for monitoring the system, ADS-equipped vehicles might 

not require a driver at all. At Level 3, the driver may engage in nondriving tasks and is not 

obligated to attend to the roadway while the ADS controls the vehicle, provided the driver is

ready to take over control of the vehicle when requested. At Level 4, there is no condition in 

which the user would be required to take control of the vehicle. If a vehicle equipped with a 

Level 4 system reaches the limits of its designated operating domain, the ADS is responsible for 

What 

does the 

human in 

the driver's 

seat have 

to do?

What do 

these 

features 

do?

Example 

Features

SAE J3016TM LEVELS OF DRIVING AUTOMATION 

SAE 

LEVEL 0

SAE 

LEVEL 1

SAE 

LEVEL 2

You are driving whenever these driver support features 

are engaged - even if your feet are off the pedals and 

you are not steering 

You must constantly supervise these support features; 

you must steer, brake or accelerate as needed to 

maintain safety 

SAE 

LEVEL 3

SAE 

LEVEL 4

SAE 

LEVEL 5

You are not driving when these automated driving 

features are engaged - even if you are seated in "the 

driver's seat" 

When the These automated driving features will 

feature requests not require you to take over driving 

you must drive 

THESE ARE DRIVER SUPPORT FEATURES THESE ARE AUTOMATED DRIVING FEATURES

These features These features These features These features can drive the vehicle This feature 

are limited provide steering provide steering under limited conditions and will can drive the 

to providing OR brake/ AND brake/ not operate unless all required vehicle under all 

warnings and acceleration acceleration conditions are met conditions 

momentary support to the support to the 

assistance driver driver 

•Automatic • Lane centering • Lane centering • Traffic jam • Local driverless • Same as level 4, 

emergency OR AND chauffeur taxi but feature can 

breaking • Adaptive cruise • Adaptive cruise • Pedals/steering drive everywhere 

• Blind spot control control at the wheel may or in all conditions. 

warning same time may not be 

• Lane departure installed 

warning 
7 

achieving a minimal risk condition (e.g., pulling off to a shoulder) without any driver assistance. 

At Level 5, which is the final level of automation, the ADS does not have a specified operational 

design domain. Instead, this system should be capable of functioning in all conditions in which a 

human driver would be capable of driving. The current review focuses on ADS or vehicles 

operating at Level 3 or higher. 

The transition from driver support systems to ADS-equipped vehicles represents a fundamental 

change in the role of both the vehicle and the driver. Much of the research in this area has been 

focused on how the introduction of ADS can provide increased safety and convenience to the 

vehicle user. However, the transition also has important implications for VRUs, especially in 

urban environments, where interactions between ADS and VRUs are likely to be most frequent. 

Ensuring that ADS and VRUs are able to interact in a safe manner is a potentially difficult task.

Utriainen and Pöllänen (2021) proposed five features that ADS-equipped vehicles require to 

interact safely with cyclists: (1) the ability to recognize bicycles, (2) the capacity to exhibit 

yielding behavior based on local laws, (3) the ability to communicate intent to cyclists, (4) the 

ability to correctly predict the cyclists’ intent, and (5) the capability to engage in safe driving 

patterns. When applied to VRUs more generally, the list creates a helpful framework for 

understanding the potential benefits and challenges associated with ADS-VRU interactions and 

highlights the potential role that infrastructure may have in helping promote VRU safety. 

Recognize VRUs

The first step to successful ADS-VRU interaction is the necessity for ADS to successfully detect 

and classify VRUs. This task can be challenging for several reasons. The first reason is VRU 

size. VRUs tend to be considerably smaller than the passenger vehicles that surround them on the 

roadways. Even the largest VRUs—such as users of motorcycles, scooters, or bicycles—will 

have rather small profiles, especially when traveling in the same or opposite direction as another 

vehicle (as opposed to traveling across traffic). In fact, most collisions between passenger 

vehicles and both bicycles and motorcycles occur when those VRUs are traveling parallel to a 

vehicle (Barnett et al., 2020; de Craen et al., 2014). VRUs must be detected regardless of the 

background environment, which might be cluttered with buildings, signs, vegetation, and other 

road furniture, especially in urban areas. 

Once detected, VRUs must be classified and distinguished from other nonmoving objects in the 

environment. The task of VRU classification is complicated by the wide range of VRU shapes. 

The profiles of bicycles, scooters, strollers, and wheelchairs are all different, but they are all 

equally important to detect. Likewise, the characteristic gait pattern of healthy adults will not be 

present among pedestrians who are using a mobility assistance device, such as a cane or walker, 

or among pedestrians who are carrying heavy packages. The reflectivity of any specific VRU can 

also vary, based on the clothing they are wearing or the time of day. Overcoming these potential 

challenges has proven difficult, especially during early attempts at VRU detection (Turner et al., 

2007). More recent work indicates that technologies’ ability to correctly detect VRUs is 

improving but has yet to be perfected (Tafidis et al., 2019). 

ADS-equipped vehicles use different types of sensors—including a camera, light detecting and 

ranging (LiDAR), radar, Global Positioning System (GPS), and sonar—to obtain information 

about the surrounding environment. Different sensors vary in their effectiveness at detecting 
8 

VRUs. Combs et al. (2019) examined the potential for ADS-equipped vehicles that use different 

types of sensors to reduce the rates of pedestrian fatalities. The authors virtually reconstructed 

5,000 crashes that occurred with a reported pedestrian fatality by using sensor technologies that 

are expected to be available in ADS-equipped vehicles to determine how many of these crashes 

could have been avoided by ADS using each detecting technology. For the purposes of this 

study, the authors assumed that accurate pedestrian detection would be the only potential barrier 

preventing an ADS-equipped vehicle from being involved in a collision. Five potential 

combinations of sensors were tested: camera; LiDAR; radar; a combination of camera and 

LiDAR; and a combination of camera, LiDAR, and radar. Dramatic differences in each 

technology’s ability to prevent the recreated fatal crashes were found, ranging from less than 

30 percent with cameras alone to more than 90 percent with the combination of camera, LiDAR, 

and radar. Combinations of technologies proved more successful than an individual technology, 

but even the combination of all sensors was not sufficient to prevent all reported crashes. 

Advances in sensor detection and classification technology are likely to aid in VRU detection in 

the future. However, until those advances become a reality, changes in infrastructure may be able 

to help bridge the gap. Much like human eyes, autonomous vehicle (AV) sensors are susceptible 

to deficits during adverse weather and in poor lighting conditions (Ye et al., 2021). Therefore, 

the installation of adequate lighting in areas with high VRU traffic could benefit both 

ADS-equipped vehicles and conventional drivers (Olszewski et al., 2015). Because VRU 

detection and classification requires differentiating VRUs from the background environment, 

limiting the amount of clutter, including signing, near the roadway could also improve detection.

Yield Based on Local Laws 

To function safely in their interactions with VRUs, ADS should behave in a manner that 

complies with all transportation policies and laws, especially laws related to yielding to, and 

stopping for, VRUs. However, this task might be more difficult than it appears. In the United 

States, yielding laws for pedestrians and bicyclists vary by State. ADS that intend to operate in 

different areas of the country may need to change their operation based on their current location. 

An obvious example of these differences among States can be seen in bicycle passing laws. All 

States specify that vehicles that are passing bicyclists must do so at a safe distance; however, the 

specific distance that is considered safe varies by State, from 2 ft in North Carolina to 6 ft for 

roads with a posted speed limit higher than 35 mph in South Dakota (Barnett et al., 2020). 

Human drivers show a willingness to break other traffic laws to comply with passing laws and 

avoid crowding a detected bicyclist. In an examination of more than 1,500 bicycle passing 

maneuvers, Chapman and Noyce (2012) found that human drivers safely crossed a solid 

centerline more than 53 percent of the time when passing a bicyclist on roads that did not contain 

bike lanes. When encountering two seemingly incompatible laws, human drivers are usually able 

to weigh the potential importance of each law and act in a way that helps ensure the safety of 

VRUs.

How ADS-equipped vehicles will react under circumstances in which State laws may be in 

conflict has yet to be determined. However, original equipment manufacturers (OEMs) who 

retain liability for ADS behaviors are unlikely to program ADS-equipped vehicles to violate any 

traffic laws. Although this exclusion of traffic law violations in the ADS programming is likely 

to benefit VRU safety in most instances, it could be problematic in situations where laws may be 
9 

in conflict, such as when the width of a lane is not sufficient to allow an ADS-equipped vehicle 

to comply with bicycle passing laws without leaving its lane. The addition of bike lanes or 

physical separation between cyclists and vehicles, which could prevent this conflicting situation, 

has already been proposed as a Manual on Uniform Traffic Control Devices (MUTCD) 

recommendation for ADS accommodation (Federal Highway Administration (FHWA), 2020). 

Bicycle laws vary by State regarding how vehicles should behave around bicyclists and how 

bicyclists can behave when interacting with vehicles (Barnett et al., 2020). For example, 

Delaware law specifies that cyclists can respond to stop signs as though they were yield signs, 

whereas in Idaho, cyclists can respond to red lights as though they were stop signs. Moreover, 

five States indicate that cyclists can pass through red lights after a reasonable amount of time. To 

navigate safely, ADS will need to be aware of these potential differences and change their 

predictions of bicyclists’ behaviors in different locations. However, expecting VRUs to always 

operate according to State laws might not be reasonable. Many VRUs are likely to be unaware of 

State-specific laws, especially VRUs who are traveling between States. Actual yielding behavior 

among VRUs has been found to be only nominally linked to formal yielding rules and policies

(Sakshaug et al., 2010). 

Determining the circumstances and extent to which ADS should yield to VRUs poses an 

important dilemma. If ADS-equipped vehicles always follow traffic yielding laws, then this 

predictability could help to enforce those laws in a manner that positively influences VRU 

behavior in the long term (Utriainen & Pöllänen, 2021). But this practice would make VRUs 

who currently fail to yield as required by law more susceptible to crashes in the short term. 

Given VRUs’ susceptibility to death and serious injury during a crash, even a short-term increase 

in crash rates would be extremely detrimental to transportation safety. Alternatively, if 

ADS-equipped vehicles take a more conservative approach by always yielding to VRUs, the 

traffic network flow would slow down, especially in high pedestrian areas. Subsequently, VRUs 

would likely learn to expect ADS-equipped vehicles to always yield, and, as a result, VRUs

might engage in more risky behavior. However, because OEMs are likely to determine actual 

yielding functions, there is the potential for variability in the way that different ADS-equipped 

vehicles respond to pedestrians. 

With repeated interaction, VRUs are likely to learn to anticipate the yielding behavior of 

ADS-equipped vehicles in different situations. Until that conditioned learning is achieved, 

infrastructure could help inform VRUs about yielding laws and encourage correct yielding 

behavior. For example, signs and crossing signals could be used to specify situations that define 

when VRUs do and do not have priority. Alternatively, ADS-equipped vehicles could help 

VRUs reduce their potential uncertainty about yielding behaviors in different situations by 

directly communicating intent. 

Communicate Intent

One method that VRUs use when trying to determine whether the driver of a conventional 

vehicle will yield is to attempt to interact with the driver. VRUs use eye contact and hand 

gestures to communicate their intent and determine whether they should attempt to cross a road 

(Fitzpatrick et al., 2016). VRUs cannot engage in this method of communication with

ADS-equipped vehicles, given that there is no driver with whom to communicate with. A large 
10 

number of studies have attempted to examine the need for ADS-equipped vehicles to 

communicate intent to VRUs and the methods that could be used to produce such

communication (Alvarez et al., 2019; Chang et al., 2018; Colley & Rukzio, 2020; Fridman et al., 

2017; Hensch et al., 2019; Lundgren et al., 2017; Löcken et al., 2019; Mahadevan et al., 2018; 

Mirnig et al., 2018). 

Most studies whose purpose was to assess the value of ADS-VRU communication have used

some type of an external human-machine interface (eHMI). EHMIs are designed to either 

communicate the intent of the ADS to the VRU or to instruct the VRU on when and how to act. 

Various eHMI types have been proposed and tested, ranging from relatively simple lightbars to 

text-based messages to animated communication images (Friedman et al., 2017). The 

effectiveness of these methods has varied. Simple eHMIs tend to be more effective than complex 

communication systems (Löcken et al., 2019), but even simple, novel eHMIs might be difficult 

for VRUs to understand. For example, when Hensch et al. (2019) attempted to use a lightbar 

affixed to a vehicle to communicate with pedestrians, most pedestrians noticed the eHMI on top 

of the vehicle but did not believe that they were being addressed by the light signal. Only 

approximately one-fourth of the participants in the study assumed that the lights represented a 

warning message to road users. eHMIs that use text messages have the benefit of not requiring 

VRUs to learn a new communication system (Chang et al., 2018; de Clercq et al., 2019). 

However, young children and adults with vision deficits may have difficulty with such messages. 

As a result, multimodal messaging is sometimes recommended (Mahadevan et al., 2018). 

The effectiveness of eHMIs has been somewhat mixed. This variation in effectiveness might be 

because VRUs overestimate how much they attempt to interact with vehicles. For example, 

surveyed bicyclists identified “accurate detection” as their primary need when interacting with

ADS-equipped vehicles, and they expressed a desire for ADS-equipped vehicles to communicate 

detection explicitly (Hegna et al., 2021). However, when Alvarez et al. (2019) tested pedestrians’ 

reactions to an ADS-equipped vehicle that was designed to explicitly communicate detection, the 

eHMI did not affect the pedestrians’ intention to cross the road. Similarly, Clercq et al. (2019) 

found that eHMI was effective at making pedestrians feel safe but only during instances when 

the eHMI indicated that it would yield to the pedestrian and not when the eHMI indicated that it 

did not intend to yield. It seems that when pedestrians identify a vehicle as ADS-equipped, they 

most likely expect that vehicle to yield, and, therefore, the pedestrians are less willing to comply 

with eHMI messages that indicate that they should yield to the vehicle. 

An alternative to eHMIs is to have ADS-equipped vehicles convey their intent to yield in a 

manner similar to that used by conventional vehicles. When judging whether to cross in front of 

a vehicle, pedestrians often use vehicle speed as a cue to determine whether they have been 

detected by the driver (Ackermann, Chemnitz & Beggiato, 2018; Varhelyi, 1998). When VRUs 

who had been involved in a crash were asked why they chose to cross the road in front of the 

vehicle that collided with them, one of the most common responses was that they saw the car 

decelerate and assumed the driver intended to yield to them, but the slowdown was actually a 

result of other traffic elements (Habibovic & Davidsson, 2012). Pedestrians express a desire to 

have ADS-equipped vehicles indicate when they are turning and stopping in a manner similar to 

the way that such information is gained from conventional vehicles (Merat et al., 2018) and seem 

to be in favor of familiar communication tools that are already used by drivers, such as flashing 

lights and honking horns (Löcken et al., 2019). If ADS-equipped vehicles convey their intent 
11 

through behaviors already employed by drivers, then the learning curve that would be required to

interact with ADS-equipped vehicles would be lessened and traveling within mixed fleets would 

become more predictable for VRUs.

If ADS-equipped vehicles are able to successfully communicate their intent to VRUs in a way 

that ensures VRU understanding and safety, then the presence of ADS could eventually remove 

the need for infrastructure intended to convey information about yielding and right-of-way. 

However, current research indicates that successful ADS-VRU communication may still be a 

long way off. Furthermore, ADS-equipped vehicles and conventional vehicles are expected to 

operate in a mixed fleet with VRUs for many years after ADS’ implementation. As a result, 

infrastructure that facilitates safe VRU-vehicle interactions is likely to be necessary for decades 

to come.

Predict VRU Intent

Ensuring ADS have the ability to correctly predict VRU intent is even more difficult than 

ensuring ADS can communicate intent successfully. Compared with vehicles, VRU movements 

are far less restricted and can change at any moment. Even in the most seemingly simple 

VRU-ADS interaction, road crossing, there are several factors that determine a VRU’s 

willingness to make a crossing (Balk et al., 2014). Once a pedestrian initiates a crossing, the 

speed, path, and even segments in which the crossing will be accomplished will vary, depending 

on the pedestrian’s capabilities and the infrastructure of the crossing. As a result, accurately 

predicting how a VRU will behave in a given situation is likely to be challenging 

(Vissers et al., 2016). 

Despite the inherent difficulty of the tasks, many AV developers are attempting to generate 

systems that are capable of predicting VRU intent (Ohn-Bar & Trivedi, 2016; 

Kumaar Jayaraman et al., 2020). One potentially promising method for achieving this goal is 

through neural networks and machine learning. Machine learning could enable ADS to combine 

information about body and head positions with key infrastructure elements to model and predict 

pedestrian crossing behavior (Kooij et al., 2014). Initial attempts to use machine learning have 

demonstrated that machine learning approaches substantially improve the classification of both 

pedestrian and bicyclist states better than nonmachine learning methods (Goldhammer et al., 

2020). One of the main barriers to these prediction models is the absence of extensive databases 

of VRU behavior that are needed to train these models (Ohn-Bar & Trivedi, 2016). However, 

with the continued improvement of pedestrian detection systems, the generation of such 

databases and the capabilities of the subsequent prediction models are likely to improve. 

The presence of specific infrastructure elements in an environment are capable of either limiting 

or facilitating VRU behaviors. As a result, the presence of infrastructure in an environment with 

high VRU traffic is likely to make the behaviors of those pedestrians more predictable. For 

example, Balk et al. (2014) found that environmental elements, such as visible countdown 

pedestrian signal, and median size, were used to form a model of pedestrian road-crossing 

behavior that correctly predicted 90 percent of the crossings in an urban area. 
12 

Safe Driving Patterns 

One of the main risks to VRUs comes from unsafe drivers. According to a seminal study by 

Singh (2015), 94 percent of vehicle crashes can be attributed to human error. Dangerous driving 

practices, such as speeding and reckless or aggressive driving, put VRUs at risk. Drivers who are 

impaired, drowsy, or distracted can be just as dangerous. For example, drivers’ failure to see or 

check for pedestrian traffic is a common source of collisions (Habibovic et al., 2013; 

Werneke & Vollrath, 2012). In a detailed analysis of 60 collisions with pedestrians and 

bicyclists, Habibovic & Davidsson (2012) found that the most common contributing factor to 

crashes recorded in the dataset was the failure of the driver to notice a pedestrian or bicyclist, 

which accounted for 80 percent of the crashes. Although some of these crashes might have been 

attributable to circumstances that even a good driver or ADS would have had difficulty avoiding 

(e.g., weather-related perceptual limitations), most are believed to be due to driver error, 

including driver impairment, fatigue, and distraction. If the majority of crashes originate with 

human drivers, then eliminating human drivers should have the potential to vastly reduce crashes 

and improve the safety of VRUs. Thus, an ADS’ ability to navigate the roadway using safe 

driving patterns is one of the greatest potential benefits of this technology. 

Thus far, ADS have not met the goal of continuous safe driving patterns. Data from reports of 

crashes that involve ADS-equipped vehicles from the California Department of Motor Vehicles 

indicate that pedestrians are still at risk (Ye et al., 2021). This continuing risk is not unexpected, 

given that ADS-equipped vehicles on the roadway are still under development. Modeling data 

that make predictions about safety and traffic flow once ADS are ready to be deployed paint a 

more optimistic picture of ADS-VRU interactions. Tafidis et al. (2019) used microscopic traffic 

flow simulation software, combined with a surrogate safety assessment model, to predict how 

bicyclists and ADS-equipped vehicles would interact within a city with a high degree of cyclist 

traffic and a 100-percent ADS market penetration. Significant reductions in both the quantity and 

severity of conflicts between cars and bikes were found. Moreover, these safety advantages were 

accompanied by improved traffic throughput for cyclists. The results point toward the 

advantages that ADS-equipped vehicles can have for VRUs. 

Although the responsibility for ADS-equipped vehicles to achieve safe driving patterns lies with 

the manufacturers of the systems, appropriate infrastructure can speed the ability of ADS to 

achieve this goal. The MUTCD notice of proposed amendments includes several 

recommendations for facilitating ADS’ driving abilities (FHWA, 2020). ADS that use machine 

vision will benefit from signing that is standardized, is parallel to the road to which it applies, 

and, when using LEDs, uses high refresh rates. Since ADS’ lane-keeping ability tends to rely on 

lane markings, ADS-equipped vehicles can benefit from lane line widths that are consistent in 

size and color. Other recommendations include that lane markings should be added along 

entrance ramps and exit ramps; along tapers, where an auxiliary lane is added; and throughout 

temporary traffic control zones, such as detours or work zones.

The five safety features that ADS-equipped vehicles need, which are outlined under the heading 

ADS in this review, provide a path toward improved interactions between VRUs and 

ADS-equipped vehicles. Are these features sufficient to ensure VRU safety? After identifying 

these features, Utriainen and Pöllänen (2021) assessed fatal bicycle crashes that occurred in 

Finland between 2014 and 2016 to determine whether the proposed characteristics could have 
13 

prevented those crashes. For most of the crashes, the successful implementation of these five 

features would have prevented the fatality. However, a minority of crashes involved situations in 

which a visual obstacle would have prevented an ADS from detecting the cyclist, and the time to 

collision after the cyclist was detected would not have been sufficient to allow the vehicle to 

come to a stop in time to prevent the collision. Preventing such crashes would require an 

extremely cautious ADS, such as an ADS that slowed down each time it neared an object that 

had the potential to obscure the view of a cyclist. Such as system would hinder traffic flow to an 

unreasonable extent. Thus, even when ADS technology is at its peak, some circumstances in 

which the technology would not be capable of preventing a collision with a VRU are expected 

(Seiniger et al., 2013). 

In cases where ADS-VRU safety cannot be achieved by ADS alone, infrastructure may be able 

to bridge the gap and help ensure VRU safety. Installation of adequate lighting and reductions in 

sign clutter in areas with high VRU traffic could help both ADS-equipped vehicles and 

conventional drivers recognize VRUs. Bike lanes and physical separation between vehicles and 

bicycle paths could help ADS-equipped vehicles comply with local laws. Treatments, such as 

signs and crossing signals that specify when VRUs do and do not have priority, could encourage 

both VRUs and ADS-equipped vehicles to yield, based on local laws. Although the ability of 

ADS-equipped vehicles to successfully communicate their intent directly to VRUs might limit 

the need for future infrastructure treatments, at present, the presence of infrastructure elements in 

the environment could help to make VRUs’ movements more predictable for both 

ADS-equipped and conventional vehicles. Moreover, consistent signing and lane markings are

likely to facilitate ADS-equipped vehicles’ ability to achieve safe driving patterns. One 

additional capability that has the potential to improve ADS safety is cooperative communication.

CDA

Literature on the capabilities of ADS when interacting with VRUs indicates areas where ADS 

can improve VRU safety, but there are limits on the technology, which could put VRUs at risk. 

The addition of cooperative communication or CDA technology could help to mitigate those 

risks. Vehicles equipped with CDA are capable of transmitting and receiving safety and 

navigation information (Yang et al., 2017). CDA vehicles are able to connect and communicate 

with other CDA vehicles through vehicle-to-vehicle (V2V) communication, and with 

infrastructure and VRUs, through vehicle-to-infrastructure (V2I) and vehicle-to-pedestrian (V2P) 

communication. CDA communication between unspecified devices that are not within vehicles is 

referred to as machine-to-machine (X2X) communication. 

V2V communication occurs when a CDA vehicle directly transmits information to and receives 

information from other nearby CDA vehicles. One type of information that can be shared is a 

basic safety message, which includes the current status of a vehicle, the location of the vehicle, 

and the other objects the vehicle is able to detect within its immediate vicinity (Nallamothu et al., 

2019). During conventional driving, detection of other objects on the roadway tends to be limited 

to line of sight. Drivers become aware of other road users only when they directly hear or see 

them. ADS-equipped vehicles can use different types of sensor technologies to detect other 

objects on the roadway. Some sensors have capacities that exceed those of human senses in 

certain conditions (Lin et al., 2019). However, as noted in the previous paragraph, the technology 

still needs to be developed further, and to some extent, the technology is still limited when a road 
14 

user is occluded by another object (Tafidis et al., 2019). In both cases, the driver or system is 

likely to be surprised by a VRU that is outside their line of sight, such as when a VRU behind a 

parked vehicle becomes visible or emerges from fog or snow. A vehicle equipped with CDA 

technology that detects a VRU could transmit the information about the location of that VRU to 

other CDA vehicles in its vicinity by offering a warning to nearby vehicles before a vehicle’s 

sensors would be capable of detecting the VRU on its own (Wolterink et al., 2010). Furthermore, 

this message transmission is not limited to other line-of-sight vehicles because vehicles with 

CDA technology are able to transmit information around curves and through obstacles 

(Yang, 2017). 

In addition to communicating directly with other vehicles, CDA vehicles have the potential to 

receive and transmit information to roadside infrastructure through V2I communication. 

Communication infrastructure that is constructed at key locations could receive safety 

information from connected vehicles and geocast relevant information to other connected 

vehicles, thereby increasing the range of messages transmitted by vehicles (Wolterink 

et al., 2010). Infrastructure could also transmit information about the upcoming roadway or 

traffic directly from local traffic management centers or smart intersections. V2P communication 

also offers the potential for CDA vehicles to communicate directly with VRUs, if those VRUs 

have access to CDA technology. 

As previously noted, pedestrian detection remains a challenge for ADS-equipped vehicles. 

Fatalities can often occur during conditions in which adverse weather and poor lighting might 

reduce sensor detection abilities (Habibovic & Davidsson, 2012). There are also fatal crashes in 

situations in which the area between the vehicle and the pedestrian is obstructed by other 

vehicles, vegetation, or buildings (Utriainen & Pöllänen, 2021). CDA communication offers the 

potential to make CDA vehicles aware of the presence of a VRU in these situations from either 

infrastructure that detects the presence of the VRU and conveys that information to the vehicle or 

from signals sent directly from a VRU that carries a CDA-capable device. Even as AV sensors 

become more adept at accurately detecting humans, VRU safety could benefit from the 

redundancy coming from a combination of perceptual and cooperative technologies 

(Merdrignac et al., 2017). 

The potential for CDA technology to benefit VRUs through direct V2P and X2X communication 

has led to several proposals on how to equip VRUs with CDA technology, including smart 

attachments to bicycles, smartphone apps, and GPS sensors designed to communicate with 

infrastructure that then communicate with vehicles (Ordell et al., 2017). Information exchange 

can occur in several ways. Pedestrian detection systems integrated into the infrastructure at 

intersections and unsignalized midblock crossings could be used to then inform oncoming 

drivers and CDA vehicles about pedestrians who might be obscured by a cluttered roadway. 

Communication from infrastructure to personal smart devices could help pedestrians better 

understand when they do or do not have crossing priority. For example, Khosravi et al. (2019) 

highlighted the potential for smartphone applications that use X2X technology to benefit 

pedestrians with visual impairments. The application used CDA technology to read signal phase 

and timing information to determine when it was safe for pedestrians to cross a signalized 

intersection, and then the application used audio and haptic alerts to convey that information to 

pedestrians. V2P communication could also be used by bicyclists and pedestrians to receive 
15 

alerts or warnings when they are in close proximity to other road users, including buses and other 

motorized vehicles and bicycles (Hincapié-Ramos & Irani, 2013). Providing VRUs with 

information about potential points of conflict could enable them to alter their behaviors in a way 

that improves safety. For example, an alert about a vehicle approaching from around a curve 

could prevent a pedestrian from starting to cross an intersection, an audio alert could help a 

pedestrian with visual impartments detect the presence of vehicles from a greater distance, or an 

alert about a motorist approaching from behind could prompt a bicyclist to move closer to the 

curve. 

CDA technologies also offer the possibility for directly communicating with VRUs who may be 

distracted. For example, visual notifications displayed on a CDA-capable mobile device could 

communicate with pedestrians who are looking down at their phones. Rahimian et al. (2016) 

found that pedestrian notification systems displayed to a texting participant were able to prevent 

the increase in collisions and risky crossings that typically occur when a participant is texting. 

However, participants who used the notification system also spent less time looking at traffic 

than participants in either the control (no texting) group or the texting without notifications 

group, suggesting that participants provided with a notification system may reduce the amount of 

attention they pay to the road when crossing. This reduction of attention has also been seen 

among drivers using collision avoidance systems (Dotzauer et al., 2015). Furthermore, once a 

user has learned to rely on such systems, reductions in attention have been found to transfer to 

situations in which the alert system is no longer in use. Discovering ways to ameliorate 

potentially dangerous effects of X2X systems on attention is likely to be an important goal of 

future research.

Another challenge associated with X2X communication is providing VRUs with access to CDA 

technology. Bicyclists in Norway and the Netherlands showed some support for the potential 

benefits of a bike-mounted eHMI that could communicate with ADS. However, potential users 

were averse to cost and theft risks of on-bike eHMIs. Moreover, VRUs were hesitant about 

personally bearing the burden of safety, preferring to leave the responsibility to ADS

(Hegna et al., 2021). Similarly, bicyclists in the United States expressed a willingness to 

purchase connectivity equipment if doing so would increase their safety, but that willingness 

varied, depending on how willing they were to purchase other biking equipment and the 

estimated price of the technology (Patil, 2016). If connectivity and other safety devices are only 

adopted by bicyclists that can afford them, then the safety of low-income riders, who may be in 

most need of nonvehicle transportation options, could be compromised. 

Equity concerns are important when considering the feasibility of CDA technology for 

improving pedestrian safety. Because both a transmitter and receiver are required for the transfer 

of CDA information, the technology is predicted to be most effective at high levels of market 

penetration (Yang & Fisher, 2021). The rate of market penetration is likely to lag behind in 

low-income socioeconomic areas, where the cost of the technology may be prohibitive. 

Depending on how the technology is implemented, CDA technology may also be less accessible 

to persons with physical or cognitive disabilities. 

Overall, the potential benefits of CDA are promising. However, very little testing or 

implementation of this technology has been done, and the actual extent of CDA benefits will not 

truly be realized until more real-world testing is available (Kockelman & Li, 2016). The limited 
16 

testing that has been done, however, suggests that current iterations of CDA technology have the 

most benefit when used under their specific operational domain and conditions (Yang & Fisher,

2021). Furthermore, some research has shown that the safety benefits of CDA technologies will 

only be fully realized at a 100-percent market penetration (Tafidis et al., 2019).

Nevertheless, CDA technologies have many benefits. The primary benefit is the potential to 

reduce injuries and fatalities through crash severity and crash rate reduction (Anaya et al., 2014; 

Mahadevan et al., 2019). In addition, there are substantial cost reductions associated with the 

reduction in crash rates and crash severity. CDA technologies could potentially reduce costs 

associated with crashes by as much as $126 billion per year, with the greatest potential savings 

being associated with forward collisions in tandem with cooperative adaptive cruise control, 

which is estimated to save $22 billion per year (Kockelman & Li, 2016). Furthermore, the 

benefits of CDA can extend to improving the safety of the vehicle operators as well as 

pedestrians and other VRUs on the roadways. Primarily, the benefit to VRUs from CDA 

technologies in comparison to ADS in general is that CDA can gain information from the 

environment beyond that available to ADS sensors and use that information to help avoid 

potentially dangerous ADS-VRU interactions (Anaya et al., 2014). 

EXPERT OPINIONS

In December 2021 and March 2022, the FHWA Human Factors Team brought together a panel 

of VRU, driving automation, and CDA experts to discuss the potential benefits and challenges 

associated with VRU-ADS interactions. Five subject matter experts (SMEs) from industry, 

government, nonprofit, and academia backgrounds participated. 

The first panel meeting began with all members introducing themselves and identifying what 

they believed were the most critical safety issues related to ADS-equipped vehicles and VRUs. 

To facilitate the discussion, the Human Factors Team then presented brief background 

information on ADS and CDA technologies and on the potential interaction with VRUs, once 

this technology is implemented within urban areas. The team also commented on the potential 

role of infrastructure in increasing VRU safety. Next, the team posed three questions to help 

facilitate and guide the panel discussion:

1. What current problems between vehicles and VRUs do you think ADS-equipped vehicles 

and CDA technology have the potential to solve? 

2. What new problems could the introduction of ADS-equipped vehicles and CDA 

technology bring for VRUs? 

3. What changes in infrastructure, including built infrastructure and TCDs, could help to 

mitigate problematic interactions between ADS-equipped vehicles and VRUs? 

Based on a discussion of these questions, the team generated a list of research gaps, which 

the panel members ranked based on the importance of prioritizing research on the specific topics. 

A final panel meeting enabled the Human Factors Team to gain consensus on the prioritized 

list of research topics and to identify additional research gaps that might be a concern for 

ADS-VRU safety. 
17 

The overarching concerns of SMEs regarding the safety of VRUs in the context of the emerging 

use of ADS-equipped vehicles and CDA vehicles ranged from unrealistic VRU expectations to 

inefficient VRU detection. The following issues were identified by the SMEs as potential 

safety-critical issues: 

• Mismatched expectations. There is a gap between how VRUs would expect 

ADS-equipped vehicles and CDA vehicles to interact with them and how this fleet of 

vehicles may actually be designed to interact. Expectation mismatch was identified as a 

cause of misunderstanding that might result in an inappropriate reaction by both sides. 

• Certain demographic groups. Older adults and people with mobility-related disabilities 

might need more time to cross a road than a typical pedestrian. The reaction of 

ADS-equipped vehicles and CDA vehicles in such situations was deemed unclear, hence 

causing concern for safety professionals. 

• Rapid accommodation expectations. The ADS industry is expecting rapid 

accommodation of built infrastructure and TCDs. A swift moving demand on traffic and 

safety standards can cause insufficient investigation on the safety aspects of these 

changes in infrastructure. 

• Detection sensors dependence. ADS-equipped vehicles are heavily dependent on 

detection sensors to identify conflict points and VRUs. Detecting VRUs, especially in 

crowded urban environments, is challenging for existing sensor technology that is 

reported in the literature or is being tested by different transportation agencies. The 

robustness of the detection systems used by ADS OEMs may be a concern because 

details of their effectiveness have not been disclosed by the OEMs. 

• VRU variations. VRUs across different ages, different needs, and different accessibility 

requirements need different accommodation by ADS-equipped vehicles. Cultural 

practices and differences in State laws lead to differences in how VRUs behave 

in different locations. ADS-equipped vehicles, which are likely to travel between 

different locations, will need to behave safely when interacting with VRUs despite 

these regional differences.

• ADS-equipped vehicle prioritization. Current literature suggests a bias toward the 

introduction of and prioritization of the needs of ADS-equipped vehicles over the 

potential needs of other road users. Therefore, before initiating infrastructural expansions 

to accommodate ADS-equipped vehicles, there should be research to measure the 

feasibility of these changes and the safety benefits of such accommodations across all 

road user types, including the potential effect on conventional drivers and VRUs. 

• Solutions organization. Dividing proposed topics into problems and solutions can provide 

a roadmap for how different agencies and authorities can approach meeting the identified 

research needs.

Advanced vehicle technology offers several safety and operational benefits. By removing driver 

distraction, reckless driving, driving under the influence, and other risky behaviors, the overall 
18 

transportation network can become safer. Moreover, reduced congestion resulting from this 

technology would contribute to operational efficiency. Yet, many questions remain unanswered 

and warrant further investigation. Whether ADS and CDAs will be capable of responding to 

these concerns remains an open safety challenge.

PRIORITIZED LIST OF RESEARCH NEEDS

The following list of research needs was generated and prioritized based on reviewed literature 

and SME ratings and discussions. The list consists of several potential concerns that are likely to 

result from ADS-VRU interaction as well as some potential solutions that could help mitigate 

these concerns.

Areas of Concern

ADS-equipped vehicles and CDA technology offer several potential benefits to the 

transportation system beyond that which can be achieved with conventional vehicles. However, 

there are also a number of potential concerns related to how these vehicles will interact with 

VRUs. The field would benefit from additional research on these concerns and the potential role 

of infrastructure in mitigating these concerns. 

Equity Issues Related to Access to Automated and Cooperative Technology 

Persons with certain types of disabilities have limited mobility in the current transportation 

system. ADS and CDA technologies have the potential to be especially useful to this group, 

provided the implementation of the technology includes appropriate accommodations. Research 

examining the usability of ADS and CDA technology for persons with physical and cognitive 

disabilities could help to identify needed accommodations and help facilitate more equitable 

technology implementation. 

VRU Detection Limitations and the Potential for Infrastructure to Support VRU Detection

Research indicates that there are limits to VRU detection abilities that may put VRUs at risk 

(Utriainen & Pöllänen, 2021). Can appropriate infrastructure use CDA technology to supplement 

VRU detection in a way that increases VRU safety?

Determining and Evaluating New Conflict Points Between VRUs and ADS-Equipped Vehicles

ADS-equipped vehicles are likely to be used by rideshare and delivery service companies. The 

locations where such vehicles pick up and drop off passengers and goods have the potential to be 

new points of conflict between ADS-equipped vehicles and VRUs. Many roadways, especially in 

crowded urban environments, do not have locations designed to accommodate pedestrian 

pickups and dropoffs. As a result, pedestrians engaging in these activities are likely to be in 

conflict with both ADS-equipped vehicles and traditional traffic. Furthermore, without sufficient 

infrastructure, ADS-equipped vehicles may resort to blocking bike lanes. Research is needed to 

determine new locations where VRU-ADS conflict points may occur and what, if any, 

infrastructure can be used to mitigate conflict and increase VRU safety.
19 

Variability in VRU Capabilities Based on Factors Such as Age and Mobility Capabilities

Older VRUs are often the most vulnerable to injury and fatalities (NHTSA, 2020). When 

implementing crash countermeasures, it is often the most vulnerable VRUs that must be 

accommodated. ADS-equipped vehicles that make predictions about VRU intent based on 

typical pedestrians may leave older pedestrians and pedestrians with limited mobility at risk. 

Research that assesses VRU behavior will need to include a wide variety of VRUs if it is to be 

effective in improving safety. 

Evaluation of the Impact of Bias in VRU Safety 

Research on the interaction between ADS and VRUs often includes an assumption that 

transportation agencies will make changes to infrastructure and shared spaces that prioritize the 

needs of ADS-equipped vehicles. However, transportation agencies may not have the means or 

desire to make such changes. Research is needed to measure the potential safety benefits of 

creating infrastructure that accommodates the needs of ADS-equipped vehicles and the 

feasibility of implementing such changes across a variety of roadway types, particularly 

roadways with high levels of VRU traffic.

Issues Related to VRUs Interacting Within Mixed Fleets

ADS-equipped, CDA-capable, and conventional vehicles are expected to all occupy the roadway 

together within a mixed-fleet environment. Therefore, VRUs may be unaware of the type of 

vehicle they are interacting with at any given moment. How vehicle-VRU interactions may 

change in mixed-fleet environments is an area with a great deal of potential research.

VRU Gap Acceptance for Automated Vehicles

VRU safety during road crossings is dependent on pedestrians selecting safe gaps between 

vehicles. Research suggests that gap selection can be influenced by vehicle type and size 

(Klatt et al., 2016). Current research suggests that many pedestrians overestimate the detecting 

capabilities of ADS-equipped vehicles (Horrey et al., 2021). Will that overestimation translate to 

riskier crossing decisions when interacting with ADS-equipped vehicles, and can 

countermeasures be implemented that reduce risky crossing behaviors? 

The Government’s Role in Ensuring Appropriate Levels of ADS Technology 

The introduction of new driving technology onto the Nation’s roadways raises questions about 

who will be responsible for ensuring the technology being developed has undergone adequate 

safety testing before being deployed. In the United States, requirements for testing and crash 

reporting are being issued at the State level, with some States, such as California, issuing strict 

guidelines for ADS testing and other States providing little to no guidance to OEMs 

(Ye et al., 2021). The potential roles that government organizations such as FHWA, NHTSA, 

and other transportation agencies will play in generating standards for ADS deployment and 

collision reporting have yet to be determined. Research that examines the effectiveness of 

different State policies could be used to guide decisionmaking at a national level. 
20 

Potential Solutions 

The concerns outlined in the previous section have the potential to be mitigated by advancements 

in technology and the implementation of infrastructure countermeasures. Some additional 

potential solutions that warrant further investigation are outlined in this section.

Rules, Standards, and Regulations for VRU and ADS Interaction 

The introduction of ADS-equipped vehicles into spaces currently occupied by VRUs, in 

particular, for those spaces related to curbside interactions between VRUs and ADS-equipped 

vehicles, is likely to generate conflicts and questions of right-of-way that go beyond what has 

previously been legislated. New rules, standards, and regulations could provide VRUs and ADS 

manufacturers with guidelines to help navigate this new space and mitigate conflict. Specifically, 

there may be a need for new rules for right-of-way and passing when ADS-vehicles interact 

with VRUs, curb management policies and regulations, and standards for pickup and dropoff 

zone design. 

X2X Communication and the Potential Benefits and Challenges Associated With Shared 

CDA Data 

Cooperative (X2X) communication offers many potential benefits for VRUs, and many open 

research questions, particularly as X2X communication relates to infrastructure. Questions range 

from the type of information that should be conveyed to the timing and manner of the conveyed 

information, to the recipient for whom the information will be most beneficial.

Educating and Managing VRUs’ Expectations Regarding ADS Capabilities 

Research suggests that VRUs may be prone to overestimate the capabilities of ADS-equipped 

vehicles (Horrey et al., 2021). Furthermore, in the absence of a dangerous conflict, trust in 

vehicle automation appears to increase with increased exposure (Penmetsa et al., 2019). 

Educating VRUs about ADS capabilities could be one way to help manage VRU expectations 

and trust in a way that encourages safe ADS-VRU interactions.
21 

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