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Simulator and on-road testing of truck platooning: a systematic review

Abstract

The evolution of transport technologies, marked by integrating connectivity and automation, has led to innovative approaches such as truck platooning. This concept involves linking multiple trucks through automated driving and vehicle-to-vehicle communication, promising to revolutionize the freight industry by enhancing efficiency and reducing operational costs. This systematic review explores the current state of truck platooning testing literature, focusing on simulator and on-road tests. The objective is to identify key scenarios and requirements for successfully developing and implementing the truck platooning concept. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) guidelines, we searched the Web of Science and Scopus databases, leading to the inclusion of thirty pertinent articles encompassing simulation-based, on-road, and mixed-environment experiments. In addition to the type of testing environment, these articles were assorted into three groups corresponding to their main thematic scope, human-centered, technology-centered, and energy efficiency studies, each providing unique insights into core themes for the development of truck platooning. The results reveal a commonly preferred platoon formation consisting of three trucks maintaining a constant speed of 80 km/h and a stable distance of 10 m between them. Simulator-based studies have predominantly concentrated on human factors, examining driver behavior and interaction within the platooning framework. In contrast, on-road trials have yielded tangible data, offering a more technology-driven perspective and contributing practical insights to the field. While the literature on truck platooning has grown considerably, this review recognizes some limitations in the existing literature and suggests paths for future research. Overall, this systematic review provides valuable insights to the ongoing development of robust and effective truck platooning systems.

1 Introduction

As part of the evolution of transport systems, the increasing integration of advanced technologies promises to revolutionize traditional paradigms. Among these innovations, the truck platoon concept aims to harness the power of connectivity and automation to reshape the efficiency and safety of road freight transport. Truck platooning is defined by the European Automobile Manufacturing Association as “the linking of two or more trucks in convoy, using connectivity technology and automated driving support systems.” [1] Pioneering studies on this concept trace back to the mid-90 s, showing from the beginning to be practical and effective [17], since then, studies have intensified due to the significant advances in automation and communication technologies.

Truck platooning promises many advantages for the drivers and the wider population. The proximity of trucks within a convoy optimizes aerodynamics, resulting in a reduction in fuel consumption [53]. As advancements in automated driving progress, the potential for safely reducing distances between trucks further enhances fuel efficiency [30]. This positive ripple effect extends to a consequential expected decrease in CO2 emissions [5] and operational costs of approximately 20% compared to traditional methods [50]. Integrating cutting-edge technologies, including autonomous driving systems and advanced sensors, and effective radio communication among trucks significantly enhances road safety. This technological amalgamation mitigates errors, reduces reaction times, and manages distractions, thus addressing the persistent challenge of a shortage or decline in professional truck drivers [7].

However, it is imperative to acknowledge that these advancements are predicated on ideal conditions, a scenario rarely encountered in real-world settings. Numerous non-constant variables, such as platoon position, inter-vehicle gaps, cargo loads, and traffic conditions, underscore the complexity of implementation [38, 47]. Delving deeper into the intricacies of platoon coordination reveals additional concerns, such as travel times, schedule miss costs, and the impacts of route divergences [56]. Consequently, the critical determinant of success lies in the widespread acceptance and validation by all stakeholders involved. This spectrum includes industrial decision-makers, end-users of the technology, and the general public, who inevitably interact with the technology externally [29]. The path to achieving these validations necessitates rigorous technology testing under diverse conditions [34].

Due to the need for research around this new concept, several projects have been developed in Europe over the years. The Promote Chauffeur I project, one of the pioneers, and its continuation, the Promote Chauffeur II, showed the feasibility of truck platooning, initially with two trucks using a tow-bar evolving into a platoon of 3 trucks [40]; the German KONVOI project [25] and the European SARTRE project [11] tested various mixed traffic scenarios on motorways and the relationship between passenger vehicles and trucks. More recently, the ENSEMBLE project opened the door to adopting multi-brand truck platoons in Europe, aiming to improve energy efficiency, road safety, and performance (Platooning [39]).

Nevertheless, following the truck industry’s early enthusiasm [21] and the ambitious goals defined in roadmaps published during the past decade [1, 14], which envisioned truck platooning deployment in public roads by as early as 2023, those goals have been gradually postponed as new challenges have arisen [22]. On the one hand, automated driving systems capable of putting the human driver “on-the-loop” or even “out-of-the-loop” are still evolving towards maturity, in a process of technology development across different stages that the Working Group on Connectivity and Automated Driving of the European Road Transport Research Advisory Council (ERTRAC) expects to be boosted by infrastructure-vehicle cooperation [15]. The maturity of automated driving systems is crucial to reduce the intra-platoon gaps, hence reducing aerodynamic drag and improving energy efficiency, without compromising safety [49]. On the other hand, the optimization of the road freight transport system goes beyond the optimization of trucks individually [16]. As so, in addition to vehicle technology development, truck platooning implementation is being hampered by (i) the lack of digital infrastructure and demonstrated business models to operate and share the benefits of the technology (e.g., allowing for flexible multi-brand and multi-operator configurations), (ii) the lack of unified regulatory frameworks to enable information sharing and prevent security threats, and (iii) by the acceptance of drivers and logistics operators about the ways such challenges are addressed, which may result in the reconfiguration of their daily activities.

As industry and policymakers are now more aware of all the complexities involved in truck platooning deployment, it is necessary to synthesize what has been previously investigated, the main outcomes, and the gaps to be still addressed, opening ways to new research recentered on the recently identified challenges. The Portuguese TRAIN project emerged as motivation for developing this work, as this project seeks to identify requirements for developing truck platooning technology and services, and assess the risks for a safe implementation in the real world through tests in a truck simulator.

The central aspect of our research involves a systematic analysis of previous studies testing the truck platooning concept. Although, under the TRAIN project, we aim to exclusively conduct driver simulator experiments, we do not neglect on-road experiments in this review, with the primary focus of obtaining a comprehensive overview of the requirements for creating truck platooning scenarios. In this way, we propose to analyze and contrast different concept tests to obtain diverse scenarios and environment configurations for testing and validating the technology. To achieve this, a systematic review following the PRISMA framework [37] was carried out, focusing on studies that provided a concrete and detailed methodology of the developed experiments.

The remainder of this article follows a structured format: the methodology used is described in Sect. 2; the main results obtained are compiled in Sect. 3, and their discussion is made in Sect. 4; in the last section, the main conclusions of this study and some considerations about future research are presented.

2 Methods

Truck platooning is an emerging technology that is not yet commercially implemented. However, truck platooning tests have been conducted using driving simulators or on-road tests. The latter occurred almost always in controlled environment (closed courses), conducted either in public roads or test tracks. In this systematic review, we followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) 2020 guidelines and checklist [37] to identify relevant literature addressing truck platooning testing. This approach involves different methodological steps: material selection, descriptive analysis, and thematic analysis.

The material selection was based on a literature search in two databases: Web of Science (WoS) and Scopus. Both databases were searched in October 2023, gathering all journal articles, conference articles, authored book chapters, and project reports written in English. All publications in other languages were excluded. WoS and Scopus were chosen to gather the studies as both have comprehensive coverage, high-quality content, citation indexing, advanced search capabilities, and global representation. Our study specifically focused on interactions between humans and trucks; therefore, only studies providing empirical evidence of such interactions were considered, leading to the exclusion of computational simulation articles to maintain alignment with the targeted scope of our research. This study does not impose time barriers and considers all studies until October 2023, enabling a comprehensive analysis of the evolving integration of truck platooning, given the recent nature of the topic. To gather pertinent data, specific key concepts were identified, including “truck”, “platoon”, “convoy”, “simulator”, “emulator”, “virtual”, “naturalistic”, “on-road”, “controlled environment”, “real environment”, and “field test”. The formulation of our research query involved combining these terms and their variations, resulting in the following query:

(truck*) AND (platoon* OR convoy*) AND (simulat* OR emulat* OR replicat* OR virtual* OR naturalistic* OR "on-road*" OR "control* environment*" OR "real* environment*" OR "field* test*") NOT (robot* OR tractor*).

This query was used simultaneously in both databases, having some minor platform specification variants. This final query evolved from the feedback of continuous experiments using different configurations of critical concepts and Booleans. The final query returned 523 articles for evaluation, which were reviewed independently by one author through some diverse stages using the Rayyan software [36]. Firstly, the software filtered duplicated articles automatically, and errors were identified by examining the metadata of all reports. Furthermore, the titles were checked, and all articles that addressed truck platooning and a concrete environment were included for screening.

In Fig. 1, the PRISMA flow diagram demonstrates all stages. Of the initial 523 articles identified through our query, 131 were flagged as duplicates or did not comply with the specified criteria for the publication type (e.g., literature reviews and abstracts only). Because of that, these articles were excluded from our analysis. In the next stage, 114 records were removed following a title review. In the subsequent phase, the 278 remaining articles were screened by reading the abstract, excluding 241 records. From 37 records sought for retrieval, five had unavailable full texts, resulting in 32 articles eligible for full-text screening. In this specific stage, nine records were excluded for various reasons. Although the title and abstract are written in English, the body of some records were in a different language (German, Japanese, or Korean), while others fell outside the scope of our research topic, including military vehicles, scale vehicles, and buses. Additionally, articles lacking self-report metrics, such as scenarios and validation tests, were also excluded. The remaining 22 articles underwent cross-referencing in the concluding phase, resulting in the inclusion of eight additional articles following a comprehensive analysis of their full text. Consequently, we included 30 articles in this review after completing all stages, facilitating a more in-depth analysis in the following phases.

Fig. 1
figure 1

PRISMA Flow diagram

For the descriptive analysis, all formal attributes of the 30 selected articles were compiled, including the title, authors, publication year, publisher, number of citations, country of research, sample size (total, men, women), methodology, goals, conclusions, limitations/gaps, and future research. This data was entered into a spreadsheet, processed, and exported as a data frame into a Python Integrated Development Environment (IDE) for analysis. Further in this review, whenever feasible, an explanatory analysis of the variables is illustrated using visual representations, such as graphs or tables. These visual aids depict the frequencies and percentages of the variables under consideration. Moreover, a graph representation was crafted to illustrate the interconnection among articles based on their references.

In the last step, each article underwent a thorough examination to extract pertinent information. The studies were categorized into distinct groups that enabled a thematic analysis of truck platooning testing. This categorization was achieved by considering two dimensions: the testing environment and the scope of the study. The categorization of the testing environment distinguishes between simulator-based trials, on-road tests, and a combination of both. In relation to each study’s thematic scope, it can fall under one of the following categories: human-centered studies, technology-centered studies, and energy efficiency studies. The integration and refinement of these diverse categories resulted in a consolidated synthesis of the studies’ findings and elucidated each category’s unique conclusions and limitations.

3 Descriptive analysis

Although most developments in truck platooning have occurred since the 2010’s with the fast uptake of Advanced Driver Assistance Systems (ADAS), the first article featuring tests within the scope of automated truck platooning dates back to 1995. The majority of the sampled studies, accounting for 26 articles (86.7%), were published from 2010 onward. The remaining studies were published in 2009 (2 articles; 6.7%), 2004 (1 article; 3.3%), and 1995 (1 article; 3.3%). The year with the highest number of publications (5 articles; 16.7%) was 2014. The publication timeline is presented in Fig. 2.

Fig. 2
figure 2

Yearly distribution of truck platooning studies (1995–2023)

The distribution of articles by publication type reveals a composition of journal articles (18; 60.0%), conference articles (11; 36.7%), and technical reports (1; 3.3%). Notably, most of these publications are were published by the Institute of Electrical and Electronics Engineers (IEEE), highlighting its role as a prominent platform for disseminating research on truck platooning.

The studies exhibit a concentration in Europe (15; 50%), East Asia (12; 40%), and North America (3; 10%), with a notable prevalence in Japan (10; 33.3%) and Germany (6; 20%) (Fig. 3). The concentration of studies in a few key regions underscores the limited geographic scope of investigations in the field.

Fig. 3
figure 3

Reviewed articles by country of the study setting

Nearly all articles established connections with other studies on truck platooning, with the most frequently cited articles within our sample being Tsugawa et al. [48] and Kunze et al. [26], each linked to five different articles, being also some of the most cited articles from our 30 final articles in the searched databases, with 243 and 76 Scopus citations, respectively. Figure 4 provides a visual representation of these relationships through a Python script we created that analyzes each article’s bibliography and visually represents it through graphical connections, illustrating the interconnection of the articles. The arrows are directed from the citing article to the cited article.

Fig. 4
figure 4

Network of article connections

4 Thematic analysis

The analysis of the 30 articles revealed that an important determinant was the testing environment in which the studies were conducted. As a result, the studies were systematically categorized based on the truck platooning test environment. This categorization distinguishes between studies conducted in a simulator environment, on-road, and incorporating both settings. Additionally, the articles were further segmented by thematic scope: (i) human-centered studies, where the primary focus is on the driver, including both truck and peripheral drivers, (ii) technology-centered studies, focusing on the vehicle and associated technologies, including ADAS, communication between vehicles (V2V), algorithms for truck platooning, etc., and (iii) energy efficiency studies, addressing strategies to reduce fuel consumption and mitigate gas emissions. A condensed representation of the adopted categorization is presented in Table 1. In the appendix (Tables 5, 6 and 7), we describe the details of the reviewed studies, including the objectives, employed methodologies, sample characteristics, conclusions, identified limitations, and proposed directions for future research.

Table 1 Studies categorized by test environment and thematic scope

4.1 Driver simulator studies

Driver simulators represent a flexible and cost-effective solution for analyzing diverse human factors and technology issues in transport research. Their adaptability allows for evaluating and optimizing human performance within system constraints, providing insights into potential problem areas in system design and functionality. Simulators prove particularly valuable in selecting a viable system approach from multiple alternatives and assessing system performance before actual field testing. Experiment-specific scenarios can be easily crafted to meet the unique requirements of each study, while avoiding safety risks of real-world tests [43, 45, 46].

We analyzed ten articles that exclusively utilized simulator tests (Table 5), and three additional articles that complemented the research with on-road tests (Table 7). Upon deeper examination, these studies collectively evaluate truck drivers’ acceptance and behavioral aspects toward the concept of truck platooning. Various dimensions are explored, such as drivers’ behavior and adaptation to truck platooning [32, 52], their representations of different levels of automation [20, 59], and their reactions during transitions between manual and automated driving [55].

Three studies concentrate on the significance and effectiveness of information visualization through Human–Machine Interfaces (HMI), both in the technology development stage and during tests to evaluate drivers’ acceptance [12, 13, 57]. In turn, two studies focus on the drivers of surrounding vehicles in truck platooning scenarios, assessing their adaptation and reactions to critical situations involving platoons of trucks [19, 52]. The level of detail presented in these studies influences the depth of the analysis, as some articles meticulously outline their methodologies and scenarios, while others concentrate solely on objectives and draw conclusions based on them (see Tables 5 and 7).

4.2 On-road tests

On-road studies, which include studies conducted in public roads or proving grounds, provide more practical insights in relation to simulator-based studies, revealing adaptability to different road infrastructures and everyday occurrences, leveraging the invaluable factor of real-world experience. Besides three articles conducting truck platooning testing in both simulated and on-road environments (Table 7), 17 of the reviewed studies focusing exclusively on on-road environment have been characterized (Table 6). This category comprises human-centered studies that assess driver behaviors within the framework of truck platooning [8, 9, 51], accounting for unpredictable everyday factors such as traffic, obstacles, and meteorological conditions [7,8,9], and evaluating and validating the utilization of HMI [18, 26].

On-road tests emphasize the study of technology-related aspects, particularly highlighting the importance of establishing and maintaining vehicle-to-vehicle (V2V) communication [17, 27, 31] and developing control algorithms for truck platooning to ensure operations safety [24]. Additionally, energy efficiency analysis is also prominent in on-road tests, where most of the studies aim to validate computational simulations regarding fuel economy, showing the effectiveness of truck platooning at an economic and environmental level [3, 4, 6, 10, 30, 48, 49].

The three studies that use a mix of simulated and on-road tests includes exploring aspects related to braking safety [2, 58],after undergoing simulations in a controlled environment where risks are absent, some scenarios are subsequently tested in real-world road experiments [41] or in controlled environment [2, 58].

4.3 Human-centered studies

Human-centered studies refer to the research primarily aimed at understanding the interactions between humans and vehicles in the context of truck platooning. These interactions may refer to the drivers of the platooning trucks and the drivers of other vehicles that may share the road with truck platoons.

A common approach to analyze the driver behavior of truck drivers involves employing a two-truck platoon configuration while maintaining a constant speed of 80 km/h. The main focus of research of these analyses is on the reactions of the following driver to varying gaps to the vehicle ahead [7, 32], considering different settings in relation to traffic density and climate conditions [20], and acceptance of Human–Machine Interfaces (HMIs) [13, 57]. The gaps considered in these studies range from four to 22 m.

The interaction with HMIs is particularly relevant in the context of driver distraction. While real-time access to information from other trucks on the platoon can ease the acceptance and use of the system, HMIs can also become sources of distraction [45, 46]. Given the combination of automation and long driving periods, studying the potential distractions drivers face is crucial to guarantee a safe operation of truck platooning [23]. The importance and visual awareness of HMIs can be examined during regular interactions such as merging, platooning, and splitting [13, 18]. Using top-view cameras and sensors, researchers can provide visual displays of distances and traffic, enhancing the driver’s situational awareness. Alternatively, investigating the impact of "see-through" technology, which allows drivers to see the traffic ahead of the lead truck, could reveal its effectiveness in improving traffic monitoring and reducing reaction times to critical events [12, 54].

Together with distraction, drowsiness and passive fatigue are concerns related to automated driving that are even more relevant in long journeys [44], as it is the case of long-distance freight transport, the main application of truck platooning. Such phenomena are challenging to measure, considering the impacts of counteracting strategies potentially adopted by drivers [28]. In combination with sleepiness, Hjälmdahl et al. [20] measured the workload, trust, driving acceptance, and driving performance during a 45-min drive test under three different conditions: baseline, partial automation, and full automation. The authors concluded that automation increases workload, decreases trust and acceptance, and leads to higher levels of sleepiness. The proposed solution involves creating an HMI that addresses driver sleepiness, without overburdening the driver, while satisfying legal requirements. [8, 9] analyzed the use of an HMI through eye-tracking data during 2-h trials on a German motorway. They concluded that drivers did not have significantly higher subjective sleepiness ratings nor substantially reduced situation awareness.

The reviewed studies also incorporated tests designed to examine scenarios where the driver alternates between the roles of leader and follower [7,8,9]. Key parameters such as the number of trucks and the speed are maintained, with the follower truck maintaining a gap of 15 m or 21 m to the leader [8, 9].

To compare the differences between manual and autonomous driving of trucks in a platoon, Zheng et al. [59] conducted tests using a simulator. The trials in manual mode were conducted with distances between trucks ranging from 20 to 30 m. In contrast, the trials in autonomous mode utilized distance intervals of 4, 8, and 12 m, demonstrating that automation allows for a safe reduction of the gap between trucks. Regarding transitions between driving modes, tests were performed based on the event’s criticality, which could be a critical/emergency or a non-critical/routine transition situation. Zeng et al. (2014) studied these transitions in both a simulator and field tests, focusing on critical events with a constant gap of 10 m. Additionally, Zang et al. (2019) tested transitions in critical and non-critical situations, varying the gap between 0.3 and 0.8 s.

Regarding the interactions of light vehicles with truck platoons, Gwak et al., [19] specifically explored the reactions of those vehicles in scenarios involving truck platoon emergency stops and the merging of platoons onto a highway, considering platoons of three simulated trucks traveling at a speed of 80 km/h. Yang et al. [52] explored the dynamics of light vehicles interacting with multiple truck platoons, mainly focusing on highway entry and exit maneuvers. The research centered on scenarios where truck platoons exclusively occupied the rightmost lane, each consisting of four or eight trucks. The study also considered variations in inter-platoon distances, set at 50 m or 100 m, to understand how these configurations affected the driver behavior.

It was possible to observe that most truck platooning studies set platoon speeds at 80 km/h, matching the posted speed limit for heavy vehicles in many European highways. In contrast, in the USA, trucks can travel at the same speed as passenger cars, which is about 20% higher than in Europe [42]. Nevertheless, in the case of human-centered studies, Ramakers et al. [41] tested platoons at lower speeds, varying from 60 to 80 km/h, with four trucks and a distance gap of 10 m. Yang et al. [52], tested the highest speed settings (90 km/h), involving up to eight trucks and a time gap as low as 0.4 s.

Table 2 consolidates the requirements regarding the scenarios outlined by the studies, including the number of trucks in platoon, the speed of the trucks, and the distance between them, expressed either in time, space or both. Of the 17 articles approaching human-centered issues, only 15 are depicted in Table 2, as Castritius et al. [7], and de Bruijn & Terken [12] do not allow to extract these specific requirements, despite their relevance to the understanding of truck platooning testing that deemed their inclusion in the current review.

Table 2 Experimental setting of human-centered studies

4.4 Technology-centered studies

Technology-centered studies deal with the technical feasibility of truck platooning, mainly focusing on the development of longitudinal and trajectory control systems enabled by V2V communication to ensure the safety of trucks traveling closely in convoy. Franke et al., [17] pioneered in the demonstration of the concept of truck platooning. Using an instrumented truck following a leading van and optical sensing, the authors developed field tests to compare a scenario without V2V communication and a distance gap of 8 m with a scenario with V2V communication and a distance gap of 4 m. According to the authors, the technical feasibility of truck platooning was shown in both cases, although the driving is less precise and less comfortable in the scenario without communications.

Nevertheless, V2V communication is currently regarded as a key component of truck platooning, allowing the development of the adaptive cruise control (ACC) into the cooperative adaptive cruise control (CACC) [33]. The CACC reduces the response delay in relation to the preceding vehicle, enabling a stable coordination of the longitudinal movement in convoy at higher speeds. In this way, Mikami & Yoshino [31] built a field test to evaluate 5G capabilities for truck platooning in terms of latency and capacity. Low-latency V2V communications for vehicle coordination and vehicle-to-network (V2N) communications for remote platoon monitoring were tested for a wide range of platooning speeds, from 10 to 90 km/h, with a constant gap of 10 m.

Beyond the analysis of drivers’ reactions to varying platoon gaps, discussed in the previous section, Aki et al. [2] studied emergency breaking from the perspective of braking systems’ reliability. The authors developed an improved safety brake system for truck platooning aimed at retaining safety for simultaneous V2V communication and brake system failures. This technology, involving a redundant braking system and a brake force adjusting device to compensate for different reaction times of the platooning drivers in case of emergency, was developed using a mix of on-road and simulated tests with configurations consistent with the human-centered studies. The on-road tests used three trucks running in parallel lanes, for safety concerns, at a speed of 80 km/h, and gaps varying between 10 and 20 m. Simulator experiments involved two trucks separated by a 10-m gap.

In turn, Kaneko et al. [24] and Lee et al. [27] focused on the development of algorithms for trajectory control. The former authors developed a real-time trajectory generation algorithm for automated truck steering in platooning context, incorporating both a risk potential driver model and a vehicle dynamics model. The algorithm was implemented in a real truck and validated in a field test considering obstacle avoidance and merging into a platoon of two trucks. This experiment introduces a third element into the platoon, with varying entry speeds of 60, 70, and 80 km/h. Considering that the following vehicles’ path planning is impaired by the short gap and front-view range, Lee et al. [27] proposed an algorithm to allow each following truck to compute the trajectory of the front part of the leading vehicle, using V2V communication, and use it as its own target path. Two trucks travelling at 80 km/h with a small gap of 0.7 s are used to test the algorithm on a test track, evaluating its performance in everyday situations, including unintended steering input, single lane changes, and curves that may lead to off-tracking.

Kunze et al. [26] have also concentrated on developing a generic software architecture for a Driver Information System (DIS) to optimize the organization and operation of truck platoons. This system supports route planning, truck selection, and the execution of maneuvers required to form and split platoons, aiming to automate and streamline these processes to enhance the efficiency and safety of truck platooning operations. As Kunze et al. [26] do not allow to extract the specific requirements described in Table 3, this table summarizes the experimental settings of the remaining five studies mentioned in this section.

Table 3 Experimental setting of technology-centered studies

4.5 Energy efficiency studies

Energy efficiency studies consist of an important part of the literature reporting to truck platooning testing, as reduced fuel consumption and emissions have been the main motivators for the proposal of truck platooning in the first place [53]. Considering that the lower the gap between trucks, the lower the aerodynamic drag, and consequently, the lower fuel consumption and emissions, it is not surprising that a notable diversity in gap distances is apparent within energy efficiency studies, reflecting a predominant focus on comparisons where gaps serve as the variable factor. While, on the one hand, gap distances varied between 3 and 44 m in energy-efficiency tests, on the other hand, speeds exhibited a remarkable consistency, hovering closely around the legal limits for heavy vehicles on highways in different regions. A distinction emerges between European and Asian studies, where speeds ranged between 80 and 90 km/h, and those conducted in the US, where speeds fall within the 55–65 mph range (89–105 km/h).

The experiments by Browand et al. [6], Alam et al. [3], Davila et al. [10], and Alam et al. [4] share similar configurations, typically involving two or three vehicles in the platoon, speeds ranging between 75 and 90 km/h, and gap distances or time spanning from 3 to 15 m or 1 to 5 s, respectively. These studies achieved fuel savings ranging from 2 to 15%. In contrast, the studies by Tsugawa [49] and Ramakers et al. [41] employed configurations with four trucks and a broader gap range extending from 4 to 30 m. Tsugawa [49] reported average fuel savings of 13–18% with empty trucks and 8–15% with loaded trucks, while Ramakers et al. [41] did not present specific results.

Further experimentation by McAuliffe et al. [30] included evaluating more aerodynamic trucks, serving as a comparative benchmark. The authors explored various configurations, combining speed changes (ranging from 88 to 105 km/h), changes in the gap between trucks (including trials with gaps ranging from 17 to 44 m), and alternating between standard and aerodynamic trucks. The aerodynamic trucks were, in practice, based on standard with add-ons such as side skirts and boat tails. The study revealed that the entire vehicle platoon’s net fuel savings was between 5.2% and 7.8%. No significant change in fuel savings was observed beyond a 22-m gap in standard trucks, with average fuel savings of 5.2%, and beyond a 34-m gap in aerodynamic trucks, with average fuel savings of 5.7%.

Despite analyzing only two and three truck configurations that maintain a 10-m gap and a speed of 80 km/h, the study by Tsugawa et al. [48] stands out for its detailed scene-level specifications. The range of tested scenarios included the formation of platoons with 2 and 3 trucks. Additionally, the study simulated a passenger car cutting in ahead of the lead truck while reducing speed, requiring a deceleration of the entire platoon. Furthermore, it examined the smooth integration of a passenger car between the second and third trucks without disrupting the platoon, causing the third truck to adjust its speed accordingly. The study also addressed scenarios involving obstacle detection by the lead truck leading to a lane change of the entire platoon, and instances where the lead truck braked manually to a complete stop, prompting automatic stops by the following trucks.

The results of Tsugawa et al. [48] indicated significant fuel savings ranging from 7.5% to 18%, with an average of 14%. Furthermore, the research highlighted a reduction in CO2 emissions ranging from 2.1% to 4.8%, underscoring the environmental benefits of truck platooning under various operational conditions. Table 4 describes the main experimental settings and fuel savings results of the eight energy efficiency studies described.

Table 4 Experimental setting of energy efficiency studies

5 Summary

The compilation of studies within the human-centered, technology-centered, and energy efficiency themes offers a comprehensive overview of current research in truck platooning. In this way, as in other driving contexts, simulators emerge as a preferred tool when investigating the intricate interactions between humans and technology. This preference arises from the capability to craft specific scenarios, manipulate variables systematically, and observe specific behaviors without real-world risks, an aspect that is even more relevant when the testing of new technology is at stake [45, 46]. In the context of truck platooning, driver simulators many times focus on two-vehicle configurations [32, 54, 59], as utilizing only two vehicles in simulations is enough to ensure visual congruence for drivers. However, a prevalent shift is observed upon transitioning to real-world environments, with the predominant use of three vehicles [24, 30, 48, 51]. This shift reflects the importance of optimizing savings in actual driving conditions.

Accounting for all environments, the most frequently employed configuration, yielding optimal driver acceptance and economic considerations results, involves a convoy of three heavy vehicles. This setup maintains a platoon speed of 80 km/h (approximately 50 mph) and ensures a consistent distance of 10 m between vehicles, equivalent to a mere 0.4 s of separation, aligning with industry goals [35].

Simulated experiments predominantly unfold on motorways characterized by two lanes, occasionally extending to three lanes, and spanning a range between 2 and 10 km in length. In contrast, on-road experiments encompass distances ranging from 60 to 150 km, with the activation of the platooning function never exceeding 82 km.

Simulator studies are used to understand driver behavior, acceptance of automation, and the effectiveness of HMIs. Studies such as Nakamura et al. [32] and Yang et al. [52] explore how drivers adjust their behaviors and trust in automation systems. Researchers also develop and test algorithms for V2V communication and ACC systems using driving simulation, as seen in Zheng et al. [59] and Hjälmdahl et al. [20]. Additionally, driver simulators have also been used to assess potential fuel savings of different platooning configurations and aerodynamic drag conditions quickly and economically, laying the groundwork for predicting fuel efficiency gains and environmental benefits [41].

On-road studies have the advantage of allowing to analyze truck platooning under real-world conditions. These studies enable practical validations of simulator findings and address complex environmental variables such as traffic dynamics, weather conditions, and road infrastructures. Research by [8, 9] and Yang et al. [51] focuses on driver behavior, workload management, and safety considerations during actual platooning operations. On-road tests also validate the performance of critical technology components for the implementation of truck platooning, such as V2V communication systems and control algorithms, as conducted by Franke et al. [17] and Lee et al. [27], also providing empirical evidence of fuel savings achieved through optimized platooning configurations on actual highways, validating earlier simulator predictions.

Concerning mixed-environment studies, they offer a balanced approach to evaluating truck platooning technologies across controlled and real-world conditions. These studies explore driver behaviors and acceptance of automated systems in transitioning between simulated and on-road environments. For instance, Ramakers et al. [41] found that drivers and freight forwarders’ acceptance of platooning systems increased across their research that included surveys, driving simulators, and on-road tests. Mixed-environment studies have also been used to test components from the technology and the driver’s perspective, as it is the case of braking systems [2, 58] and HMIs [26, 58], taking advantage of the different levels of risk allowed by simulation or on-road experiments. Specifically, mixed-environment studies investigate how drivers adapt to sudden changes in traffic dynamics and emergency scenarios, and validate the integration and interoperability of V2V communication systems and control algorithms across different testing environments, ensuring platooning technologies’ seamless operation and safety under varying conditions.

Human-centered studies are naturally more focused on scenarios envisioned for the near future, where automation levels do not go beyond levels 2 or 3, and the human driver is still kept “in-the-loop” (the lead driver controls the dynamic driving task), or, at least, “on the loop” (the lead or follower drivers have to monitor the automated driving systems). In this way, the analyzed scenarios maintain greater distances between trucks for enhanced safety and driver comfort. Researchers like de Bruijn & Terken [12] and Dreger et al. [13] delve into how drivers interact with automated systems and adapt to varying levels of autonomy. These studies focus on reaction times, comfort levels, and situational awareness during platooning maneuvers. The scenarios in these studies are the most elaborate, involving external vehicles and more complex maneuvers, highlighting the importance of ergonomic design and driver training.

In turn, the scenarios of technology-centered studies are generally more precise and rigorously detailed, given that the objectives of research are strongly related to technology reliability and safety concerns. These studies mainly develop and test algorithms for V2V communication and automated driving systems within the context of truck platooning. Researchers such as Kunze et al. [26], Kaneko et al. [24], and Lee et al. [27] focused on optimizing control algorithms that govern vehicle spacing and speed coordination within platoons. These studies validate the technical feasibility of maintaining safe distances and smooth interactions between platooning trucks under various conditions. On-road tests by Franke et al. [17] and Lee et al. [27] assessed the real-time responsiveness and reliability of truck platooning technologies in dynamic traffic scenarios, addressing challenges such as obstacle detection, emergency braking, and lane merging.

Energy efficiency simulator and on-road studies seem to complement each other. While the potential energy efficiency gains of different platooning configurations and aerodynamic drag conditions can be easily estimated through simulation, on-road studies such as Tsugawa [49] and Davila et al. [10] provide empirical evidence of fuel savings achieved through optimized platooning configurations on actual highways. These studies measured aerodynamic drag reduction, fuel consumption rates, and emission reductions, validating earlier simulator predictions and quantifying environmental benefits.

6 Conclusions

In the pursuit of the optimization and decarbonization of the road freight transport sector, substantial efforts have been undertaken towards a real-world implementation of truck platooning, notably propelled by the truck industry and by European authorities and policymakers [1, 14, 21]. However, while previous studies and demonstrators provide intricate details and specifications elucidating the concept of truck platooning, it is imperative to approach the generalization of requirements with caution and be mindful of certain inherent limitations related to safety and security, physical and digital infrastructure, business models, and impacts on the labor market [16]. This systematic review examined several truck platooning studies, with focus on previous simulator and on-road testing and demonstration of this emerging concept, providing a comprehensive overview of the current state of the literature. The main objective of this review was to compile the various requirements for truck platooning testing through a thematic analysis of different approaches and environments.

The relationship between humans and technology has always been complex, and truck platooning works in the same way. Simulators are primary tools for exploring these dynamics, allowing customized scenarios and real-time adjustments without damage or repercussions, but cannot replace studies in real environment and field tests to demonstrate truck platooning as a feasible concept to adapt to different road infrastructures and everyday occurrences. In this way, we argue that the best practices for truck platooning testing, either as part of a single or a sequence of studies, combine integrating and testing solutions on a simulator and then deploying them to on-road tests.

In line with the endeavor to enhance the optimization road freight transport through infrastructure-vehicle communications [16], the reviewed studies highlighted technology issues such as interference waves, insufficient 5G throughput, and latency increases due to unsupported handovers. Feedback and communication delays were noted as impacting safety. Hardware and operational challenges, such as high costs, technology vulnerabilities, human–machine interactions, and the lack of maturity of automated driving systems, were also identified as significant barriers to truck platooning deployment.

Regarding the experimental setting of truck platooning tests, a platoon configuration of three heavy vehicles, maintaining a speed of 80 km/h and a consistent distance of 10 m between vehicles seems to ensure a balance between driver acceptance and economic considerations, while adhering to industry standards. More precise details and specifications depend on the specific motivation and objectives of each study. Research design and participant-related limitations were common, including small sample sizes, skewed demographics, and scenarios that did not accurately reflect real-world conditions. Some studies raised concerns about the ecological validity of driving simulators and the short duration of non-driving task engagements. Methodological constraints included lacking quantitative support for qualitative results and focusing on non-critical transitions and single critical events. One emerging consideration for future truck platooning demonstrations, which is directly related to the representativity of the most diverse real-world situations, is the extension and quality of the road network suitable for platooning operations. Although the existing studies delve into truck platooning with a focus on technical aspects, a critical need exists for dedicated investigations centered on road analysis. This line of research would shed light on the infrastructure prerequisites for a successful truck platooning implementation, considering factors such as topography, lane width, and exposure to inclement weather. The knowledge of the road network available for platooning operations has a direct impact on the assessment of costs and benefits, and consequently on the definition of business models and use cases.

Although not specifically addressed by the reviewed studies, legal and societal challenges have been widely acknowledged as important factors limiting the widespread adoption of platooning systems. Legislation governing truck operations exhibits significant variability across different countries and is far from ready to accommodate platooning operations. Among the main regulatory barriers are traffic laws dictating minimum distances between heavy vehicles and establishing maximum speeds, and labor laws governing working schedules and resting periods. This legal diversity represents a layer of complexity that needs to be addressed, but careful steps are required to achieve a good regulatory framework applicable to both national and cross-border transport. To prevent safety and security risks, ensure seamless and optimized truck platooning operations, and safeguard adequate labor conditions, it is understandable and commendable that regulators demand for a detailed and definitive demonstration of the technology impacts, benefits, and reliability, confirming the need for further human factors and technology-centered research.

In short, there has been significant progress in making truck platooning a reality. While recognizing these advancements, the current recommendations reflect the high complexity of optimizing road freight transport as a whole, on the pathway to the decarbonization of this sector, and how can truck platooning contribute to this endeavor. The insights gathered in this review provide a foundation for future research in truck platoon testing and demonstration, based on what has been done so far, the achieved results, and the identified gaps, towards robust, effective, and integrated applications for this technology.

Data availability

The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.

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Funding

This research was funded in whole by the Fundação para a Ciência e a Tecnologia, I.P. (FCT, Funder ID = 50110000187) under the project with https://doi.org/10.54499/PTDC/ECI-TRA/4672/2020 and the grant with https://doi.org/10.54499/CEECINST/00010/2021/CP1770/CT0003.

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Telmo Botelho: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft. Sérgio Pedro Duarte: Conceptualization, Formal analysis, Writing – review & editing, Supervision. Marta Campos Ferreira: Conceptualization, Methodology, Supervision. Sara Ferreira: Writing – review & editing, Project administration, Funding acquisition. António Lobo: Methodology, Formal analysis, Writing – review & editing, Project administration, Funding acquisition.

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Correspondence to Sérgio Pedro Duarte.

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Appendix

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See Tables

Table 5 Characterization of driver simulator studies

5,

Table 6 Characterization of on-road tests

6 and

Table 7 Characterization of studies conducted in both simulated and on-road environments

7.

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Botelho, T.C., Duarte, S.P., Ferreira, M.C. et al. Simulator and on-road testing of truck platooning: a systematic review. Eur. Transp. Res. Rev. 17, 4 (2025). https://doi.org/10.1186/s12544-024-00705-6

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