Exploring Potential Critical Content of Connected and Autonomous Vehicles for Transportation Engineering Courses: A National Survey

Abstract

As technology advances, there is a growing demand for understanding the fundamental concepts of connected and autonomous vehicles (CAVs) and their relative impact on different aspects of transportation engineering. Various transportation courses are regularly offered in civil engineering programs, including but not limited to an introduction to transportation engineering, transportation planning/modeling, highway design, transportation safety, traffic engineering, traffic simulation, and intelligent transportation systems. Instructors often face challenges in identifying critical CAV topics for these courses, making it necessary to explore critical course content related to CAVs. The objectives of this study are (1) to identify critical CAV topics for different transportation engineering courses and (2) to emphasize upcoming CAV topics crucial for a successful transition into the CAV system. To achieve these objectives, a national survey was conducted among transportation educators and practitioners. The survey was distributed with the assistance of Transportation Research Board Standing Committees to ensure broad participation. Among the 48 respondents from 33 states, faculty members who had already integrated CAV topics into their courses and practitioners were prioritized while estimating the weighted average of each topic. The findings of this research can serve as valuable resources for redesigning transportation engineering courses by incorporating CAVs. The identified high-priority CAV topics can play a crucial role in raising awareness among undergraduate and graduate students about the diversity of transportation engineering and motivating them to explore emerging CAV technologies and opportunities within the field.

Keywords

policy and organization executive management issues transportation workforce management training and workforce development university education of future workforce workforce development

With the advancement of technology, there has been an increasing demand for learning the fundamental concepts of connected and autonomous vehicles (CAVs). Graduates entering the transportation engineering field need knowledge of diverse topics, including CAVs. University civil engineering programs regularly offer various required and elective transportation engineering courses, including, but not limited to, an introduction to transportation engineering, transportation planning/modeling, highway design, transportation safety, traffic engineering, traffic simulation, and intelligent transportation systems (ITSs). Instructors often face challenges in identifying critical CAV content for these courses, making it necessary to explore critical course content related to CAVs. This paper focuses on a national survey of transportation educators and practitioners to determine and prioritize CAV topics for different transportation engineering courses. The study results provide valuable insights and course improvement opportunities, such as expanding the content of transportation courses to incorporate evolving CAV-related knowledge.

Background and Study Objectives

This section of the paper is organized into four main parts. The first part provides a concise overview of CAVs, including their deployment status and the challenges they may pose. The second part synthesizes studies of the potential impact of CAVs in various aspects of transportation engineering. The third part highlights the current course structures and content related to CAVs offered by renowned engineering academics. The last part outlines the objectives of this study.

Brief Overview of CAVs

CAVs refer to vehicles with transformative technology that incorporate both autonomous vehicle (AV) and connected vehicle (CV) capabilities. CVs utilize wireless technologies (e.g., commercial wireless services, Bluetooth) to facilitate the exchange of information between vehicles (V2V), vehicles and infrastructures (V2I), vehicles and electronic devices (V2D), vehicles and networks (V2N), vehicles and grids (V2G), vehicles and pedestrians (V2P), and other traffic systems (V2X). The primary reason for developing such a robust communication system is to enhance traffic safety and improve traffic flow by providing vehicles with information about nearby vehicles and roadside objects ( 1 ). In contrast, AVs rely on advanced technologies (e.g., adaptive cruise control) to control steering, acceleration, and braking functions with minimal or no human involvement. The level of human monitoring required for AVs depends on their level of automation ( 2 , 3 ). For those interested in understanding the six levels of automation, further information is available on the Society of Automotive Engineers (SAE) website ( 3 ).

Research indicates that CV technology has a limitation in relying solely on message exchange for mutual awareness between vehicles ( 4 ). On the other hand, AVs rely on onboard sensors, embedded software, and artificial intelligence, which can reduce the need for external infrastructure or communication. However, AV technologies are not yet completely reliable and face challenges in extreme weather conditions or unpredictable road situations. Recognizing these limitations, the U.S. Department of Transportation (U.S. DOT) emphasizes the importance of converging CV and AV technologies, as seen in CAVs, to overcome these challenges and achieve transportation system benefits through cooperation ( 5 ).

The U.S. DOT actively supports the development of CV technology through pilot deployment programs ( 6 ). This initiative aims to identify barriers, find solutions, document insights, and provide a blueprint for future CV technology deployments. Examples of such programs include the Tampa Hillsborough Expressway Authority Pilot, New York City Pilot, and Wyoming Department of Transportation (WYDOT) Pilot. In relation to AVs, modern vehicles are equipped with at least one advanced driver-assist system (ADAS) ( 7 ). Level 2 features, offering partial automation, are commercially available in vehicles manufactured by Audi, Tesla, Lexus, Porsche, BMW, and Volvo. Furthermore, Audi has developed a traffic jam pilot system that enables steering, braking, and acceleration on highways, qualifying as a Level 3 feature ( 8 ). Meanwhile, Waymo has successfully deployed Level 4 self-driving taxis on public roads in Arizona, accumulating an impressive driving experience of over 10 million miles within a year. In addition, the development of fully AVs is currently progressing rapidly, showcasing substantial growth ( 9 ). As of February 2024, 19 out of 50 states have introduced/implemented AV legislation related to infrastructure and CVs ( 10 ). Although CAVs have several operational and safety advantages ( 11 , 12 ), the main challenge lies in aligning the existing physical and digital infrastructures to fully leverage the benefits ( 2 ). Other challenges include public acceptance, appropriate investment in CAVs, smooth technological transitions, and updating the existing policies and regulations ( 13 ).

Potential Influence of CAVs in Transportation Engineering

The potential impact of CAV technologies on highway engineering is contingent on state transportation policies and practices, and road operations. The experimental studies cited in the following discussions have provided evidence supporting the significant influence of CAV deployment across various domains of transportation engineering, including transportation planning, road geometric design, traffic safety, traffic operation, traffic simulation, and ITSs.

Transportation Planning

The convenience and productivity gains associated with CAVs may incentivize individuals to travel more, potentially increasing travel demand ( 14 ). Individuals who find CAVs comfortable and convenient may be more willing to travel longer distances ( 15 ). CAVs can enhance road capacity by enabling closer vehicle spacing and communication with traffic infrastructure ( 16 ). However, increased travel demand can also contribute to congestion and reduce the performance of the road network ( 17 ). In one study, the capacity enhancements provided by CAVs have been observed to offset the growth in demand, resulting in minor reductions in average speed ( 18 ). It should be noted that the extent of these effects is heavily dependent on factors such as AV ownership and household mobility classifications. A survey conducted in the Dallas-Fort Worth-Arlington Metropolitan Area found that a small percentage of respondents were willing to purchase a self-driving car at a higher cost than a human-driven vehicle ( 19 ). In light of this, integrating multiple transportation modes can be a more practical approach to reducing overall trip costs and congestion, and minimizing transfer inconveniences ( 18 ). According to the National League of Cities, the inclusion of CAVs in the primary planning documents of metropolitan planning organizations (MPOs) saw a significant rise from 6% in 2015 to 61% in 2017 ( 20 , 21 ). State departments of transportation (DOTs) prioritize scenario-based planning programs when incorporating CAVs into transportation planning processes ( 22 ). This approach allows participants to compare diverse CAV scenarios, examine potential operational and safety outcomes, question assumptions about the future, and make more informed decisions.

Road Geometric Design

Efficiency and safe operation of the surface transportation system greatly depends on the geometric design. The design encompasses the dimensions and arrangements of visible road features. The potential impacts of CAVs on road design elements are summarized in Table 1.

Table 1.

Impact of Connected and Autonomous Vehicles (CAVs) on Geometric Design Control Criteria

Design elements	Potential influence
Design speed	To accommodate the characteristics of AVs, the Permanent International Association of Road Congresses (PIARC) has proposed alternative frameworks for classifying service levels ( 23 ), considering factors such as infrastructure support for automated driving (ISAD) and frequency of disengagements.
Lane width	AVs are expected to exhibit precise steering control and accurate lane-tracking capabilities. Research indicates that highly connected AVs can reduce required lane widths to as low as 8 ft ( 24 , 25 ).
Shoulder width	AVs need a safe area to stop/park, wait for the driver to regain control, or for conditions to improve for the automated control system to proceed ( 26 , 27 ). Therefore, there is a need for regularly spaced wider shoulder widths that can serve as safe harbors during emergencies ( 28 ).
Bridge width	The precise movement of AVs may lead to narrower lane widths, potentially creating opportunities for constructing extra lanes or narrower bridge widths within existing bridge design guidelines. However, current bridge load models and bearing capacity guidelines have yet to account for additional lanes and vehicle platooning complexities ( 9 ).
Structural capacity	The channelization of CAVs has the potential to impose higher loading repetitions on narrower lane widths. This can contribute to the accumulation of pavement distress, ultimately reducing the structural performance of the pavement ( 29 , 30 ). However, the increased traffic speed associated with AVs would counteract this effect ( 31 ).
Vertical clearance	Areas on tunnels and underpasses may require additional or different lighting, as image-based line recognition cannot detect lines in tunnels owing to insufficient light ( 32 ). Therefore, adequate vertical clearance needs to be ensured to accommodate proper lighting systems.
Horizontal alignment	As CAVs can detect and react much faster than human drivers, the stopping sight distance is significantly reduced. Therefore, optimizing the minimum horizontal curve radius can be crucial for effectively operating automated driving systems. However, sharp horizontal curves present challenges for AVs because of limited visibility of the installed machine-vision sensors ( 33 ). As a result, maintaining a minimum curve radius could be more practical until further extensive research.
Vertical alignment	In CAVs, the eye height is determined by the placement of LiDAR sensors ( 34 ). In addition, the LiDAR replaces traditional headlights and the associated inclined angle ( 35 ). Therefore, CAVs require shorter curve lengths than conventional vehicles in both sag and crest vertical curves.
Stopping sight distance (SSD)	AVs can react promptly to obstacles, with response times ranging from 0.2 to 1 s, depending on the level of automation ( 28 ). In addition, the deceleration rate can be increased up to 14.8 $ft / s^{2}$ ( 36 ).
Horizonal clearance	The required SSD for CAVs is much lower than for human-driven vehicles and, in turn, the required safe horizontal sightline offset (HSO) distance will be lower for CAVs ( 37 ).

Note: AV = autonomous vehicle.

Road Safety

CAV technologies have the potential to save more than US$70 billion annually and preserve over 700,000 functional life-years per year ( 38 ). However, the actual effectiveness of these technologies could vary depending on market penetration and utilization rates. In a mixed-traffic environment, the conventional method of assessing safety effectiveness for a specific treatment might underestimate or overestimate its true impact ( 39 ). For example, rumble strips have proven effective in reducing run-off-road crashes. Since CAVs rely less on human reactions and decision-making, their likelihood of veering off the road is reduced. Consequently, as CAVs constitute a larger portion of the vehicle fleet in the future, the crash modification factor (CMF) associated with rumble strip treatment is expected to increase. This implies that the anticipated benefits of installing rumble strips might be diminished in scenarios where a significant proportion of the vehicle fleet is automated.

Several studies utilizing microscopic simulation models integrated with surrogate safety assessment models have found that as the proportion of AVs in the traffic stream increases, there is a reduction in crash frequencies and their associated severities ( 5 , 11 ). Since 2014, the California Department of Motor Vehicles (CA DMV) has been responsible for collecting crash data from AV manufacturers operating in California. Novat et al. ( 40 ) analyzed these AV crashes between 2017 and 2020 to compare the characteristics of AV crashes with those involving conventional vehicles. The analysis revealed that AVs were more likely to be involved in rear-end collisions and less likely to experience sideswipe or broadside incidents. Moreover, these rear-end crashes were more common at intersections. Expanding CAV testing in more complex driving environments and average roadways is essential to gain a comprehensive understanding of their safety performance.

Traffic Operation

In recent years, AV manufacturers have undertaken numerous pilot studies to develop advanced traffic sign recognition (TSR) systems with on-board sensors. However, these trials have yielded unsatisfactory results, particularly when dealing with specific road and environmental complexities. For example, camera-based recognition in support of the LiDAR system occasionally shows erroneous results in rainy weather and dark-not-lighted conditions ( 41 ). On a global scale, the diversity of traffic signs further complicates the TSR system's operation ( 42 ). Pike et al. ( 43 ) conducted a comprehensive study to explore the reliability of machine systems in detecting longitudinal pavement markings. The outcomes indicated that markings were less detectable in sun glare, specifically in wet surface conditions. To address these challenges, the National Committee on Uniform Traffic Control Devices (NCUTCD) has made efforts to identify necessary modifications to existing traffic control devices to accommodate the operation of CAVs. The CAV Task Force of NCUTCD has provided several recommendations concerning pavement markings and traffic signs ( 44 ), including increasing the contrast of painted markings, mandating restriping at specific intervals to prevent fading, ensuring good retroreflective backgrounds, and so on.

CAVs are expected to improve freeway capacity by reducing the gaps between consecutive vehicles and forming platoons through integrated speed management ( 45 , 46 ). However, these theoretical results are based on microscopic traffic simulation studies, and real-world operational data or field tests have yet to validate these hypotheses. Furthermore, the benefits of CAVs may be diminished in mixed traffic conditions because of longer platoons and rapid shock wave occurrences ( 47 ). Zmud et al. ( 48 ) proposed physically separating CAVs from manually operated vehicles to fully leverage road capacity-enhancing operations. The seventh edition of the Highway Capacity Manual (HCM) has introduced updates to capacity adjustment factors to evaluate the influence of CAVs on freeway operations, signalized intersections, and roundabouts ( 49 ).

Traffic Simulation and ITSs

Two approaches exist for modeling CAVs in VISSIM: adjusting the internal parameters of behavior models (car-following models, lane-changing models, etc.) or utilizing external interfaces such as the component object model (COM) application programming interface (API), external driver model (EDM), and driving simulator interface ( 50 , 51 ). Among these options, the external interfaces for CAV modeling are preferred because of their greater flexibility, overcoming the limitations of the built-in CAV behavior editor, such as the inability to simulate V2V and V2I connectivity ( 52 ). A crucial aspect to consider in the simulation of CAVs is how these vehicles interact with their operating environment. This entails considering various factors, such as infrastructure modeling, including links, connectors, and other network elements, as well as the behavior of CAVs at signalized and non-signalized intersections. Various factors are considered when generating different scenarios, including the market penetration rate at different levels (0%, 20%, 40%, 60%, 80%, or 100%), weather conditions (clear or adverse), traffic density (peak hours or off-peak hours), and the condition of the road infrastructure (upgraded or deteriorated infrastructure). This helps to assess how well CAVs perform under varying conditions and establish the necessary measures to integrate CAVs on roads safely.

CAV technology is expected to significantly affect traveler information, available traffic management strategies, and crash rates, leading to a demand for expanded data management and security, as well as new operational policies and practices ( 53 ). In the context of CAVs, ITSs focus more on routing techniques to establish an efficient transportation system in urban environments ( 54 ). The architecture reference for cooperative and intelligent transportation (ARC-IT) has been developed to offer guidance and standardization for developing and implementing cooperative ITSs. It includes multiple physical object diagrams for various ITS functional objects, including CAV applications ( 55 ). Within the category of vehicle safety, the ARC-IT provides physical diagrams for several CAV technologies, such as AV safety systems, situational awareness, curve speed warning, pedestrian and cyclist safety, cooperative adaptive cruise control, and AV operations.

CAV Courses Offered by Academic Institutions

Identifying the specific CAV topics covered in basic transportation engineering courses can be challenging because of limited access to the course materials. This section discusses the outline details, including specific topics in some instances, of a few CAV courses offered by renowned academic institutions. These details can be readily accessed on their institutional websites. Northwestern University offers an academic course (COMP_ENG 395/495) dedicated to CAV technologies ( 56 ). The course focuses on current trends, opportunities, limitations, and potential future directions in the field. Purdue University's Civil Engineering Department introduced a graduate-level course (CE 597: Machine Learning and Artificial Intelligence) in 2020 ( 57 ). The course focuses on applying machine learning (ML) algorithms in the context of AVs to evaluate resilient and sustainable mixed traffic streams. The course consists of instructor presentations, algorithm demonstrations, project reports, student presentations, group discussions, and guest speaker sessions. A graduate-level course (CE 8930: Autonomous Vehicle Systems) on AV systems has been continued since 2019 at Clemson University ( 58 ). The course aims to comprehensively understand AV system concepts, the current deployment status, operation, and evaluation. Key topics covered in the course include introduction to AVs, AV technologies, AV control design aspects, evaluation of AV systems, and policies, laws, and regulations for AV systems. The University of California San Diego has developed a course called “Introduction to Autonomous Systems” that is open to undergraduate students and professionals in embedded systems engineering (ESE) ( 59 ). This course aligns with Purdue University's approach, which combines ML and AV systems in real-world professional settings. Furthermore, Johns Hopkins University offers a cybersecurity program tailored explicitly for CAVs, intended for graduate students with prior experience in the field ( 60 ). The course's format highlights the significance of AV safety and the precise operational requirements of intelligent vehicles throughout their technological lifespan.

Study Objectives

The practical implementation and functioning of CAVs greatly depend on a knowledgeable, trained, and skilled workforce. As CAVs progressively replace conventional vehicles, offering various safety and operational benefits, the need for updated roadway infrastructure supporting advanced electronic assistance features and communication technologies becomes evident. The ITS Professional Capacity Building (PCB) Program is a comprehensive approach of the U.S. DOT to educate present and future transportation professionals about current and upcoming intelligent transportation technology. This program has already incorporated various educational resources on CAVs, such as hands-on learning kits and system design documents ( 61 ). Therefore, it is crucial to update and revise current transportation engineering curricula to equip students with the necessary knowledge about emerging CAV technologies and their potential impacts on different aspects of transportation engineering. The objectives of this study are (1) to identify critical CAV content for different transportation engineering courses within the civil engineering curriculum and (2) to emphasize upcoming CAV topics that are crucial for a successful transition into the CAV system. To accomplish these objectives, a national survey was conducted among transportation engineering educators and practitioners. The prioritization of CAV topics was determined based on the estimated weighted average of importance scale. The outcomes of this research can serve as valuable resources while redesigning transportation engineering courses. Moreover, the identified high-priority CAV topics can play a crucial role in raising awareness among undergraduate and graduate students about the diversity of transportation engineering and encouraging them to explore emerging CAV technologies and opportunities within the field.

Methodology

Selection of Preliminary Critical CAV Content

Initially, a comprehensive list of potential critical CAV topics is compiled based on previous CAV studies and descriptions of CAV courses offered in various universities, as briefly outlined in the preceding section. In the past one to two decades, two transportation engineering fields that have experienced significant advancements and wide-ranging impacts are traffic simulation and ITSs. Therefore, these courses have been included in the list of transportation engineering courses, along with other traditional ones. In total, 36 CAV topics have been selected to be integrated into seven transportation engineering courses. Table 2 provides an overview of these topics categorized by course. Each topic is assigned a unique identification number (ID) to facilitate easy identification and reference.

Table 2.

Preliminary Selected Critical Connected and Autonomous Vehicle (CAV) Topics by Course

ID	Topics
Introduction to transportation engineering
1Q1	What are CAVs?
1Q2	Levels of automation
1Q3	CAV control concept
1Q4	CAV legislation
1Q5	Overview of CAV pilot deployment
1Q6	Brief intro of potential CAV impacts on highway infrastructure, geometric design, transportation planning, road safety, and environment
Transportation planning/modeling
2Q1	Impact of CAVs on state/local transportation planning
2Q2	Potential changes to travel demand modeling components from CAV impacts
2Q3	Travelers’ perception of CAVs
2Q4	The influential decision factors in CAVs and their technology usage
2Q5	Expected changes in multimodal transportation after CAV deployment
2Q6	Shared mobility of CAVs
Highway design
3Q1	Impact of CAVs on geometric design components (e.g., stopping sight distance, lane/shoulder width, horizontal sight offset, vertical curves, etc.)
3Q2	CAV dedicated lane design
3Q3	Potential changes required in the geometric design standards for CAVs
Transportation safety
4Q1	Contribution of CAV technologies in improving road safety
4Q2	How road safety performance varies with CAV penetration
4Q3	Ensuring safe performance of CAVs in complex driving environments
4Q4	Current research on CAV-involved crashes
Traffic engineering
5Q1	How the detection performance of CAV technologies depends on traffic control devices (e.g., traffic signs, signals, pavement markings)
5Q2	Changes/improvements in network capacity, traffic congestion, stability of traffic flow, and travel time reliability with CAV penetration rates
5Q3	Complexities in mixed traffic streams and their relative impact on traffic flow parameters
5Q4	Impact of CAVs on other road users (e.g., pedestrian, bicyclist)
Traffic simulation
6Q1	Simulate CAV using common traffic simulation tools (e.g., VISSIM, TSIS, and SYNCHRO)
6Q2	Verification, validation, and calibration of simulation models
6Q3	Traffic simulation tools: microscopic, mesoscopic, and macroscopic in context of CAV modeling
6Q4	Vehicle dynamics simulation tools
6Q5	Traffic microsimulation models: vehicle-following, lane-changing, and gap-acceptance
6Q6	Automated driving systems and vehicle connectivity
6Q7	Interactions between CAVs and their operating environment
6Q8	Scenario generation (mixed traffic stream)
6Q9	Simulation output statistics
Intelligent transportation system (ITS)
7Q1	CAV applications in ITSs
7Q2	Architecture reference for cooperative and intelligent transportation
7Q3	CAV initiatives and programs (e.g., CAV pilot deployment program, cooperative automated transportation coalition, international harmonization)
7Q4	Connected ecosystem (e.g., roadside equipment for communications, center systems and CV data backhauls, communications/messaging standards, cyber security)

Note: CV = connected vehicle.

Online Survey Design

A national survey was conducted to identify high-priority CAV topics for transportation engineering courses and gather professional input. The survey was designed for three groups of respondents.

Group 1: Transportation engineering educators who have already incorporated CAV topics into their courses.

Group 2: Transportation engineering educators who have not included CAV topics in their courses.

Group 3: Transportation engineering practitioners.

The survey targeted transportation faculty members and practitioners with expertise in transportation planning, roadway geometric design, traffic operations, highway safety, and related areas. However, faculty members and practitioners primarily focused on pavement materials, construction management, and other branches of civil engineering not related to transportation systems were excluded from the survey. It is anticipated that Group 1 and Group 2 participants have a higher level of knowledge with respect to CAV issues than others. This is because they are more enthusiastic about CAVs and possess professional experience working with or dealing with CAVs. As a result, these individuals are likely to be more familiar with the latest developments, challenges, and solutions related to CAVs. The initial questions in the survey gathered basic participant information, including their location, job position, background, and experience. Participants were also asked if they had previously attended/instructed any CAV workshops or short courses, with the option to attach relevant materials from those events. Subsequently, participants were asked to evaluate the level of importance of each selected CAV concept in different transportation courses. A rating scale from “1” to “5” was used, where “5” indicated the highest priority for inclusion in the course and “1” denoted the lowest priority. In addition, educators had the opportunity to indicate if they already covered any of the identified CAV topics in their teaching. In each transportation engineering course, the survey provided options for participants to provide critical feedback. This included attaching lecture notes, providing references, and suggesting additional CAV topics. The survey was conducted online and designed to be completed within approximately 10 min.

Participants and Survey Distribution

The survey aimed to rank and identify the most relevant and up-to-date CAV knowledge that can be incorporated into transportation engineering courses. The target respondents were transportation engineering educators and practitioners who hold at least a bachelor's degree in civil engineering and specialize in transportation engineering, including CAVs. The survey was conducted between May 2022 and August 2022. For survey delivery, the internet-based survey tool Qualtrics was chosen. This platform provided flexibility in utilizing different question and response formats, ensuring that meaningful and representative information could be gathered from the respondents.

The Transportation Research Board (TRB) Standing Committee on Transportation Education and Training (ABG20), reorganized in 2021 into Workforce Development and Organizational Excellence (AJE15), has a common goal of improving communication and collaboration among academic, private, and government transportation communities. Its mission includes aligning educational practices with workforce needs and fostering awareness of the transportation engineering profession. The Institute of Transportation Engineers (ITE) Transportation Education Council shares similar objectives, aiming to raise awareness and support transportation educators by addressing emerging issues and alignment gaps between educational institutions and the profession. Furthermore, several TRB committees, such as the Traffic Simulation Committee (ACP80) and Freeway Operations Committee (ACP20), focus on advancing research and technology transfer in the areas of traffic simulation. To ensure broad participation, these committees helped distribute the survey among their members. In addition, several educators and practitioners with experience in CAVs were identified by exploring university and transportation research institute websites. Once identified, these individuals were contacted via email and invited to participate in the survey. Non-respondents received follow-up emails approximately three weeks after the initial survey distribution.

Results and Discussion

Number of Respondents and Their Characteristics

A total of 48 out of 65 recipients completed the survey. Among these respondents, 20 belonged to Group 1, 16 were in Group 2, and the remaining 12 were in Group 3. To ensure accuracy, the IP address of each participant and their responses were checked to prevent duplicate submissions. The geographic distribution of the respondents is depicted in Figure 1, showcasing their locations across the U.S.A. The survey respondents were from 33 states, indicating a well-distributed representation throughout the country. Multiple responses were received from recipients in 10 different states, namely California, Florida, Mississippi, Missouri, Nebraska, Pennsylvania, Texas, Utah, Virginia, and Wisconsin. Furthermore, it is worth noting that the responses received from the same state mostly covered different counties or cities, enhancing the diversity of the data.

Figure 1.

Geographic distribution of respondents.

Among transportation engineering educators who have already incorporated CAV topics into their courses (Group 1), 35% (7 out of 20) held the position of associate professor, while 30% were professors, 30% were assistant professors, and the rest of the percentage were instructors (Figure 2). The range of experience among these faculty members varied from a minimum of 1 year to a maximum of 29 years. Some 50% of these faculty members had less than 5 years of teaching experience. The 25^th percentile of experience was 3 years, the median was 5.5 years, and the 75^th percentile was 9 years. This distribution aligns with the increasing popularity of CAV concepts and related research over the last decade. In addition, 6 out of the 20 respondents had previously attended or instructed CAV workshops.

Figure 2.

Distribution of respondents belong to Group 1.

Of the transportation engineering educators who have not integrated CAV topics into their courses (Group 2), 43.8% (7 out of 16) held the position of associate professor, while 37.5% were professors, and 12.5% were assistant professors (Figure 3). The range of experience among these faculty members ranged from a minimum of 1 year to a maximum of 20 years. The 25th percentile of experience was 5.8 years, the median was 8 years, and the 75th percentile was 17.3 years. Notably, more than 80% of these faculty members had at least 5 years of teaching experience. In addition, 6 out of the 16 respondents had previously attended or instructed CAV workshops. The collective attributes of Group 2 respondents suggest a hypothesis that senior faculty members with significant teaching experience are less inclined to integrate the CAV concept into their academic courses, even if they have attended or instructed any CAV workshops or short courses.

Figure 3.

Distribution of respondents belong to Group 2.

Among the transportation engineering practitioners in Group 3, 58.3% (7 out of 12) were employed by state government agencies, while 25% worked for federal government agencies, and 16.7% were associated with consulting firms (Figure 4). Furthermore, 5 out of the 12 respondents in this group had prior experience attending or instructing CAV workshops.

Figure 4.

Distribution of respondents belong to Group 3.

CAV Topics Already Incorporated in Transportation Engineering Courses

In the survey, Group 1 respondents were queried about whether they had already included any CAV topics in their transportation engineering courses. Of the 20 respondents in this group, 17 reported including the CAV definition in their “Introduction to Transportation Engineering” course (Figure 5). Among the top 10 topics, six were from “Introduction to Transportation Engineering” (What are CAVs?; Levels of automation; CAV control concept; Brief intro of potential CAV impacts on highway infrastructure, geometric design, transportation planning, road safety, and environment; Overview of CAV pilot deployment; CAV legislation), two were from “Transportation Planning/Modeling” (Potential changes to travel demand modeling components from CAV impacts; Impact of CAVs on state/local transportation planning), and the remaining two were from “Traffic Simulation” (Simulate CAV using common traffic simulation tools) and “Transportation Safety” (Contribution of CAV technologies in improving road safety) courses.

Figure 5.

Top 10 connected and autonomous vehicle topic identification numbers already included in academic courses.

Critical CAV Topics by Ranking

As previously mentioned, the survey used a scale of “1” (lowest priority) to “5” (highest priority) for each topic. A weighted average was calculated to determine the high-priority CAV topics. Practitioners (Group 3) and educators who have already integrated CAV topics into their course materials (Group 1) tend to be more enthusiastic about CAV concepts and show greater interest in staying updated with the latest information on CAVs. Taking this into account, a weight of 1.5 was assigned to both Group 1 and Group 3, while Group 2 had a weight of 1. If respondents had not selected an importance scale for a particular topic already included in their teaching, the highest priority scale (“5”) was assigned. Topics that scored a weighted average of 4 or more were considered critical as well as high-priority. It is essential to note that the scale used for selecting high-priority topics was intentionally kept high in this study. The objective was to identify the most crucial CAV topics that need to be included in course lectures and short notes. Often, educators are faced with the challenge of covering a variety of course content within the total course duration. Because of time constraints, it might only be feasible to allocate one detailed lecture to discuss CAV topics. Therefore, prioritizing the most important topics becomes crucial for effective integration into the curriculum. Out of the 36 topics, a total of 16 critical CAV topics were selected that need to be included in various transportation engineering courses. These selected topics are highlighted in the shaded rows of Table 3. In addition, Table 3 provides the mean and standard deviation calculated for each preliminary selected topic by respondent group, along with the estimated weighted average. The estimated weighted average was used to rank each topic in relation to transportation engineering courses. For instance, in the “Highway Design” course, the topic “impact of CAVs on geometric design components” obtained the highest weighted mean of 4.25, ranking it as number 1. Among all the topics for this course, no other topic achieved a weighted mean of 4 or higher, indicating that “impact of CAVs on geometric design components” was the sole critical component selected for the “Highway Design” course. Table 4 provides a comprehensive list of potential critical CAV topics for transportation engineering courses, which includes the results obtained from the national survey. This compilation serves as a foundation for shaping potential lecture objectives concerning CAVs, providing guidance for transportation engineering educators to deliver impactful lessons. Table 5 illustrates the potential lecture objectives of CAVs for various transportation engineering courses based on the identified critical CAV content.

Table 3.

Weighted Mean of Potential Critical Connected and Autonomous Vehicle Topics

Note: SD = standard deviation. Shading rows indicate topics with a weighted average equal to or greater than 4.

Table 4.

High-Priority Connected and Autonomous Vehicle (CAV) Topics Based on the National Survey Results

Course	Critical topics
Introduction to Transportation engineering	What are CAVs?; Levels of automation; Brief intro of potential CAV impacts on highway infrastructure, geometric design, transportation planning, road safety, and environment
Transportation planning/modeling	Impact of CAVs on state/local transportation planning; Potential changes to travel demand modeling components from CAV impacts
Highway design	Impact of CAVs on geometric design components (e.g., stopping sight distance, lane/shoulder width, horizontal sight offset, vertical curves, etc.)
Transportation safety	Contribution of CAV technologies in improving road safety; Ensuring safe performance of CAVs in complex driving environments; Current research on CAV-involved crashes
Traffic engineering	How the detection performance of CAV technologies depends on traffic control devices (e.g., traffic signs, signals, pavement markings); Changes/improvements in network capacity, traffic congestion, stability of traffic flow, and travel time reliability with CAV penetration rates; Impact of CAV on other road users (e.g., pedestrian, bicyclist)
Traffic simulation	Simulate CAV using common traffic simulation tools (e.g., VISSIM, TSIS, and SYNCHRO); Interactions between CAV vehicles and their operating environment
Intelligent transportation system (ITS)	CAV applications in ITSs; Connected ecosystem (e.g., roadside equipment for communications, center systems and CV data backhauls, communications/messaging standards, cyber security)

Note: CV = connected vehicle.

Table 5.

Study Objectives of Lectures Concentrated on Connected and Autonomous Vehicles (CAVs)

Course	Lecture objectives
Introduction to Transportation engineering	• Provide a comprehensive understanding of AVs and CVs, including their integration. • Provide an overview of the current deployment status of CAVs. • Discuss the benefits and challenges associated with CAVs. • Briefly explore the potential impacts of CAVs on various aspects of transportation engineering.
Transportation planning/modeling	• Emphasize the influence of CAVs on the elements of travel demand modeling. • Highlight the endeavors made to integrate CAVs into state/regional transportation planning.
Highway design	• Emphasize the potential impact of CAVs on control criteria for road geometric design. • Incorporate insights from previous research on the infrastructure requirements necessary for CAVs. • Highlight the additional geometric design control criteria that need to be explored in future research during the transition period of CAVs.
Transportation safety	• Comprehend the safety benefits offered by CAV technologies. • Highlight the anticipated alterations in safety performance evaluation following the integration of CAVs into mixed traffic. • Understand the latest research discoveries with respect to crashes involving AVs. • Discuss the potential safety challenges associated with CAVs.
Traffic engineering	• Highlight the efficacy and challenges of traffic control device (e.g., traffic signs, signals, pavement markings) detection by CAV technologies. • Understand the potential influence of CAVs on network capacity, traffic congestion, and traffic flow stability at various market penetration rates. • Discuss the probable impact of CAVs on pedestrian and bicyclist movements.
Traffic simulation	• Briefly introduce the concept of CAV simulation and CAV simulation tools. • Explore the process of simulating CAVs using popular traffic simulation tools (e.g., VISSIM, TSIS, and SYNCHRO). • Discuss the interactions between CAVs and their surroundings in traffic simulation scenarios. • Provide insights into the verification, validation, and calibration of CAV simulation models.
Intelligent transportation systems (ITSs)	• Discuss the potential impact of CAVs within the ITS framework. • Briefly introduce the concept of connected ecosystem and its relevance to the field of CAVs. • Emphasize the opportunities and challenges that arise within the connected ecosystem.

Note: AVs = autonomous vehicles; CVs = connected vehicles.

Course	Recommended topics
Introduction to transportation engineering	CAV ethics; Data privacy problems associated with CAVs; Maintenance aspects of CAVs.
Transportation planning/modeling	CAV path planning; Cooperation of mobility and sustainability in CAV planning; Impact of CAVs on transportation equity and development patterns
Highway design	Geometric requirements of connected and autonomous trucks
Transportation safety	Impact on traffic incident management; Integration of safe systems and CAVs in the transportation network; Impact of CAVs on work-zone safety
Traffic engineering	Upcoming changes to the MUTCD because of CAVs; Impact of CAVs on car-following and lane-changing models; Impact of CAVs on conventional traffic flow theory and traffic signal control
Traffic simulation	Distinction between simulating CAV and human-driven vehicles
Intelligent transportation systems (ITSs)	Connecting adaptive signaling system with CAVs

Conclusions

In light of emerging technologies and the challenges faced by traffic networks, the need for a focused and relevant transportation engineering curriculum has become increasingly important. The integration of CAVs into transportation planning, design, safety, and operations presents the opportunity to address overall traffic management systems in the future. However, attempting to cover all potential CAV topics and address all associated concerns in a single course is unrealistic. The national survey results of this study identified crucial CAV topics that offer significant benefits in setting specific objectives for lectures concentrated on CAVs within transportation engineering courses. By incorporating these topics, lectures can be enriched, leading to greater quality and relevance for students. The updated course materials can provide valuable insights into the rapidly evolving field of CAVs and their profound impact on fundamental transportation engineering elements, creating opportunities to equip students with skills and knowledge necessary for the competitive job market. It is worth noting that there is no definitive answer to what should be included in these courses. The specific mix of follow-up courses, graduates' career destinations, and local and regional contexts, as well as the needs of the profession, contribute to the selection of unique CAV topics for different transportation engineering programs.

This study has a few limitations that may be addressed in future research. One area of improvement could be the introduction of a better justified weight factor and ranking scale to analyze the feedback and comments from both educators and practitioners, to draw more robust conclusions. Comparing the findings between educators and practitioners could also be a promising avenue for further investigation. CAVs represent a convergence of various technologies, including computer science, electrical engineering, and communication. To effectively teach CAV concepts, there is an urgent need for engineering faculty to swiftly adapt their curriculum and establish essential prerequisites. Traffic courses should encompass both traditional and emerging aspects. To broaden the scope of study, future surveys could incorporate a wider array of CAV topics, extending beyond transportation engineering, given the rapid evolution of related technologies and infrastructure. In addition, the current study covered 33 states in the U.S.A., and expanding the outreach efforts in future research could increase the sample size, leading to more comprehensive analyses and increased confidence in the results.

Footnotes

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: M.M. Hossain, H. Zhou; data collection: M.M. Hossain, H. Zhou; analysis and interpretation of results: M.M. Hossain, H. Zhou; draft manuscript preparation: M.M. Hossain, H. Zhou, R. Turochy. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is a part of a research project funded by the Southeastern Transportation Research, Innovation, Development and Education (STRIDE) Center (Project I5).

ORCID iDs

Md Mahmud Hossain

Huaguo Zhou

Rod Turochy

References

McGuckin

Lambert

Newton

Pearminee

Hubbard

Leveraging the Promise of Connected and Autonomous Vehicles to Improve Integrated Corridor Management and Operations: A Primer. Report No. FHWA-HOP-17-001. Federal Highway Administration, U.S. Department of Transportation, Washington D.C., 2017.

American Traffic Safety Services Association. ATSSA Member Business Opportunities Related to Connected and Automated Vehicles. ATSSA Safer Roads Save Lives, Washington D.C., 2017.

Society of Automotive Engineers (SAE). SAE Levels of Driving Automation^TM Refined for Clarity and International Audience. SAE International, 2021. https://www-sae-org-s.web.bisu.edu.cn/blog/sae-j3016-update.

Shladover

S. E.

Connected and Automated Vehicle Systems: Introduction and Overview. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, Vol. 22, No. 3, 2018, pp. 190–200. https://doi.org/10.1080/15472450.2017.1336053

Kockelman

Bansal

Boyles

S. D.

Levin

Bujanovic

Choudhary

, et al. Implications of Connected and Automated Vehicles on the Safety and Operations of Roadway Networks: A Final Report. Center for Transportation Research, Texas Department of Transportation, Austin, Texas, 2016.

Intelligent Transportation Systems Joint Program Office (ITS JPO). Connected Vehicle Pilot Deployment Program. U.S. Department of Transportation, 2024. https://www.its.dot.gov/pilots/

Epstein, S., J. Lu, S. Sturgess, J. Peet, and I. Petrov. AI in the Driving Seat: Key Challenges for the Future of Advanced Driver Assistance Systems (ADAS). Cambridge Consultants, UK, 2019.

Bell

The Connected Car Taxonomy Decoded: An Overview of Connected Autonomous Vehicles & Intelligent Transportation Systems. Heavy Reading, New York, 2017.

Liu

Tight

Sun

Kang

A Systematic Review: Road Infrastructure Requirement for Connected and Autonomous Vehicles (CAVs). Journal of Physics: Conference Series, Vol. 1187, No. 4, 2019, p. 042073. https://doi.org/10.1088/1742-6596/1187/4/042073

10.

National Conference of State Legislature. Autonomous Vehicles State Bill Tracking Database. National Highway Traffic Safety Administration, Washington, D.C., 2024. https://www.ncsl.org/research/transportation/autonomous-vehicles-legislative-database.aspx.

11.

Papadoulis

Quddus

Imprialou

Evaluating the Safety Impact of Connected and Autonomous Vehicles on Motorways. Accident Analysis and Prevention, Vol. 124, 2019, pp. 12–22. https://doi.org/10.1016/j.aap.2018.12.019

12.

Rouhani

O. M.

Miranda-Moreno

Simulation-Based Connected and Automated Vehicle Models on Highway Sections: A Literature Review. Journal of Advanced Transportation, Vol. 2019, 2019, p. 9343705. https://doi.org/10.1155/2019/9343705

13.

Connecticut Departement of Transportation. Preparing for Connected and Automated Vehicles Strategic Plan. U.S. Department of Transportation, Washington D.C., 2021.

14.

Rodier

Travel Effects and Associated Greenhouse Gas Emissions of Automated Vehicles. National Center for Sustainable Trasnportation, UC Davis, Davis, CA, 2018.

15.

Miller

P. E.

Di Kang

Ways to Consider Driverless Vehicles in Virginia Long Range Travel Demand Models. Report No. FHWA/VTRC 19-R11. Virginia Transportation Research Council, Charlottesville, VA, 2019. https://rosap.ntl.bts.gov/view/dot/40043.

16.

Stern

R. E.

Cui

Delle Monache

M. L.

Bhadani

Bunting

Churchill

Hamilton

, et al. Dissipation of Stop-and-Go Waves via Control of Autonomous Vehicles: Field Experiments. Transportation Research Part C: Emerging Technologies, Vol. 89, 2018, pp. 205–221. https://doi.org/10.1016/j.trc.2018.02.005

17.

Delis

A. I.

Nikolos

I. K.

Papageorgiou

Macroscopic Traffic Flow Modeling With Adaptive Cruise Control: Development and Numerical Solution. Computers and Mathematics With Applications, Vol. 70, No. 8, 2015, pp. 1921–1947. https://doi.org/10.1016/j.camwa.2015.08.002

18.

Levin

M. W.

Boyles

S. D.

Effects of Autonomous Vehicle Ownership on Trip, Mode, and Route Choice. Transportation Research Record: Journal of the Transportation Research Board, 2015. 2493: 29–38. https://doi.org/10.3141/2493-04

19.

Nair

G. S.

Dias

F. F.

Ruiz-Juri

Kuhr

Bhat

C. R.

Travel Modeling in an Era of Connected and Automated Transportation Systems: An Investigation in the Dallas-Fort Worth Area. Data-Supported Transportation Operations & Planning Center, Austin, Texas, 2019.

20.

DuPuis

Martin

Rainwater

City of the Future: Technology & Mobility. Center for City Soultions and Applied Research, National League of Cities, Washington D.C., 2015.

21.

Cottam

B. J.

Transportation Planning for Connected Autonomous Vehicles: How It All Fits Together. Transportation Research Record: Journal of the Transportation Research Board, 2018. 2672: 12–19. https://doi.org/10.1177/0361198118756632

22.

Thompson

Wagner

Morgan

Hall

Sener

Stoeltje

Pang

Transportation Planning Implications of Automated/Connected Vehicles on Texas Highways. Texas Department of Transportation, Texas, 2015.

23.

García

Camacho Torregrosa

F. J.

Lopis Castelló

Monserrat

J. F.

Smart Roads Classification. Permanent International Association of Road Congresses (PIARC), Paris, France, 2021.

24.

Machiani

S. G.

Jahangiri

Melendez

Katthe

Hasani

Ahmadi

Musial

W. B.

Safety Impact Evaluation of a Narrow Automated Vehicle-Exclusive Reversible Lane on an Existing Smart Freeway. Office of the Secretary of Transportation (OST), U.S. Department of Transportation, Washington D.C., 2021.

25.

Aryal

Optimization of Geometric Road Design for Autonomous Vehicle. Master’s thesis. KTH Royal Institute of Technology, Stockholm, Sweden, 2020.

26.

Transport Systems Catapult. Future Proofing Infrastructure for Connected and Automated Vehicles. Exploring Intelligent Mobility, UK, 2017.

27.

Saeed

T. U.

Road Infrastructure Readiness for Autonomous Vehicles. PhD disseration, Purdue University, West Lafayette, IN, 2019.

28.

Tengilimoglu

Carsten

Wadud

Implications of Automated Vehicles for Physical Road Environment: A Comprehensive Review. Transportation Research Part E: Logistics and Transportation Review, Vol. 169, 2023, p. 102989. https://doi.org/10.1016/j.tre.2022.102989

29.

Yeganeh

Vandoren

Pirdavani

Impacts of Load Distribution and Lane Width on Pavement Rutting Performance for Automated Vehicles. International Journal of Pavement Engineering, Vol. 23, No. 12, 2022, pp. 4125–4135. https://doi.org/10.1080/10298436.2021.1935938

30.

Zhou

Xue

Flintsch

Optimizing the Lateral Wandering of Automated Vehicles to Improve Roadway Safety and Pavement Life. Transportation Research Record: Journal of the Transportation Research Board, 2019. 2673: 37.

31.

Chen

Balieu

Kringos

Potential Influences on Long-Term Service Performance of Road Infrastructure by Automated Vehicles. Transportation Research Record: Journal of the Transportation Research Board, 2016. 2550: 72–79. https://doi.org/10.3141/2550-10

32.

Konstantinopoulou

Ljubotina

D7.2: Other Initiatives to Meet the Needs of Automated Cars. LAIN: Saving Lives Assessing and Improving TEN-T Road Network Safety. National Highway Traffic Safety Administration, Washington D.C., 2020.

33.

Nitsche

Mocanu

Reinthaler

Requirements on Tomorrow’s Road Infrastructure for Highly Automated Driving. Proc., 2014 International Conference on Connected Vehicles and Expo, ICCVE 2014, Vienna, Austria, 2014, pp. 939–940.

34.

Velodyne. HDL-64E S3 High Definition LiDAR Sensor – User’s Manual and Programming Guide. Velodyne LiDAR Inc, San Jose, CA, 2019.

35.

Khoury

Amine

Saad

R. A.

An Initial Investigation of the Effects of a Fully Automated Vehicle Fleet on Geometric Design. Journal of Advanced Transportation, Vol. 2019, 2019, p. 6126408. https://doi.org/10.1155/2019/6126408

36.

Welde

Qiao

Effects of Autonomous and Automated Vehicles on Stopping Sight Distance and Vertical Curves in Geometric Design. Resilience and Sustainable Transportation Systems – Selected Papers From the 13th Asia Pacific Transportation Development Conference, Singapore, 2020, pp. 715–724.

37.

Guerrieri

Mauro

Pompigna

Isaenko

Road Design Criteria and Capacity Estimation Based on Autonomous Vehicles Performances. First Results From the European C-Roads Platform and A22 Motorway. Transport and Telecommunication, Vol. 22, No. 2, 2021, pp. 230–243. https://doi.org/10.2478/ttj-2021-0018

38.

Kockelman

K. M.

Valuing the Safety Benefits of Connected and Automated Vehicle Technologies. Proceedings of 95th Annual Meeting of the Transportation Research Board, Washington D.C., 2016. https://trid.trb.org/View/1392528

39.

Lause

F. V.

Adapting Crash Modifification Factors for the Connected and Autonomous Vehicle Environment. MS thesis. University of Kentucky, Lexington, KY, 2019.

40.

Novat

Kidando

Kutela

Kitali

A. E.

A Comparative Study of Collision Types between Automated and Conventional Vehicles Using Bayesian Probabilistic Inferences. Journal of Safety Research, Vol. 84, 2023, pp. 251–260. https://doi.org/10.1016/j.jsr.2022.11.001

41.

Landa

Prochazka

Automatic Road Inventory Using LiDAR. Procedia Economics and Finance, Vol. 12, 2014, pp. 363–370. https://doi.org/10.1016/s2212-5671(14)00356-6

42.

Agashe

Chapman

Traffic Signs in the Evolving World of Autonomous Vehicles. Avery Dennison, Georgia, 2019.

43.

Pike

A. M.

Barrette

T. P.

Carlson

P. J.

Evaluation of the Effects of Pavement Marking Characteristics on Detectability by ADAS Machine Vision. National Cooperative Highway Research Program, Transportation Research Board, Washington D.C., 2018.

44.

Carlson

National Committee on Uniform Traffic Control Devices Connected-Automated Vehicle (CAV) Task Force TCD Suggestions for Automated Driving Systems (ADS) Feedback from Automotive OEMs. ITS News, 2019. https://www.itsrm.org/national-committee-on-uniform-traffic-control-devices-connected-automated-vehicle-cav-task-force-tcd-suggestions-for-automated-driving-systems-ads-feedback-from-automotive-oems/.

45.

Ghiasi

Hussain

(Sean) Qian

A Mixed Traffic Capacity Analysis and Lane Management Model for Connected Automated Vehicles: A Markov Chain Method. Transportation Research Part B: Methodological, Vol. 106, 2017, pp. 266–292. https://doi.org/10.1016/j.trb.2017.09.022

46.

Mahmassani

Autonomous Vehicles and Traffic Physics: Framing the Questions. Special Workshop on Traffic Flow Implications of Autonomous Vehicles, Northwestern University Trasnportation Center, Illinois, 2017.

47.

ATKINS. Research on the Impacts of Connected and Autonomouse Vehicles (CAVs) on Traffic Flow. Department for Transport, London, UK, 2016.

48.

Zmud

Williams

Outwater

Bradley

Kalra

Row

Zmud

, et al. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. National Cooperative Highway Research Program, Transportation Research Board, Washington D.C., 2018.

49.

National Academies of Sciences. Highway Capacity Manual 7th Edition: A Guide for Multimodal Mobility Analysis. National Academies Press, Transportation Research Board, Washington D.C., 2022.

50.

Ahmed

H. U.

Huang

A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation. Smart Cities, Vol. 4, No. 1, 2021, pp. 314–335.

51.

Olstam

J. J.

Tapani

Comparison of Car-Following Models. Computer Science, Engineering, London, UK, 2004.

52.

Evanson

Connected Autonomous Vehicle (CAV) Simulation Using PTV Vissim. 2017 Winter Simulation Conference (WSC), Las Vegas, NV, 2017, p. 4420.

53.

Krechmer

Flanigan

Rivadeneyra

A. T.

Blizzard

Van Hecke

Rausch

Effects on Intelligent Transportation Systems Planning and Deployment in a Connected Vehicle Environment. Federal Highway Administration, U.S. Department of Transportation, Washington D.C., 2018.

54.

Lobato

De Souza

A. M.

Peixoto

M. L. M.

Rosario

Villas

A Cache Strategy for Intelligent Transportation System to Connected Autonomous Vehicles. 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Victoria, BC, 2020, pp. 1–5.

55.

Architecture Development Team. ITS User Services Document. Federal Highway Administration, US Department of Transportation, Washington D.C., 2005.

56.

Electrical and Computer Engineering, Northwestern University. COMP_ENG 395, 495: Connected and Autonomous Vehicles: Challenges and Design. 2013. https://www.mccormick.northwestern.edu/electrical-computer/academics/courses/descriptions/395-495-autonomous-vehicles.html.

57.

CE 597 Machine Learning and Artificial Intelligence for Autonomous Vehicle Operation, Purdue University, 2021.

58.

Center for Connected Multimodal Mobility. CE 8930 Autonomous Vehicle Systems. 2019. https://cecas.clemson.edu/C2M2/ce-8930-autonomous-vehicle-systems/.

59.

Jacobs School of Engineering. ECE/MAE 148 Introduction to Autonomous Vehicles. 2021. https://extendedstudies.ucsd.edu/courses-and-programs/introduction-to-autonomous-vehicles.

60.

John Hopkins Whiting School of Engineering. Intelligent Vehicles: Cybersecurity for Connected and Autonomous Vehicles - 695.634. 2021. https://ep.jhu.edu/courses/695634-intelligent-vehicles-cybersecurity-for-connected-and-autonomous-vehicles/.

61.

ITS Professional Capacity Building (PCB), U.S. Department of Trasnportation. Connected and Automated Vehicle Education. 2022. https://www.its.dot.gov/pcb/CAVEducation.aspx.