Simulator and on-road testing of truck platooning: a systematic review
DOI: 10.1186/s12544-024-00705-6
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This systematic review addresses the current state of truck platooning testing, aiming to identify key scenarios and requirements for the successful development and implementation of this technology. Truck platooning, defined as linking multiple trucks via connectivity and automated driving systems, promises significant improvements in fuel efficiency, safety, and operational costs. However, widespread adoption is hindered by technical immaturity, regulatory gaps, and stakeholder acceptance issues. Motivated by the need to synthesize existing empirical evidence to guide future research—specifically within the context of the Portuguese TRAIN project—the authors analyzed literature focusing on simulator and on-road experiments to understand the complexities of real-world deployment. The study adhered to PRISMA 2020 guidelines, searching Web of Science and Scopus databases for articles published up to October 2023. The search query targeted terms related to trucks, platooning, and testing environments (simulator, on-road, field tests), excluding computational simulations and non-English publications. From an initial pool of 523 articles, the authors screened titles, abstracts, and full texts, ultimately including 30 pertinent studies that provided empirical evidence of human-truck interactions or technical validation. These studies were categorized by testing environment (simulator, on-road, or mixed) and thematic scope (human-centered, technology-centered, or energy efficiency). The review found that the most common platoon configuration in the literature consists of three trucks traveling at a constant speed of 80 km/h with a stable inter-vehicle distance of 10 meters. Simulator-based studies predominantly focused on human factors, examining driver behavior, acceptance, and interactions with Human-Machine Interfaces (HMIs). Key findings indicated that while automation can reduce intra-platoon gaps safely, it may increase driver workload and sleepiness, necessitating careful HMI design to prevent distraction. In contrast, on-road trials provided tangible data on technology-centered aspects, validating vehicle-to-vehicle (V2V) communication and control algorithms. These studies demonstrated that V2V communication is critical for maintaining safe, short gaps and improving longitudinal control stability. Energy efficiency studies, largely conducted on-road, confirmed the aerodynamic benefits of platooning, showing reduced fuel consumption and CO2 emissions. The significance of this review lies in its comprehensive synthesis of testing methodologies and outcomes, highlighting a geographic concentration of research in Europe and East Asia. It identifies a gap in the literature regarding diverse real-world variables and multi-brand interoperability. The authors conclude that while simulator studies are valuable for assessing human factors and system design, on-road testing remains essential for validating technical feasibility and energy savings under realistic conditions. The review provides a structured overview of requirements for future research, emphasizing the need for robust testing frameworks that address both technological maturity and human acceptance to facilitate the commercial deployment of truck platooning.
Key finding
Simulator-based studies predominantly focus on human factors and driver behavior, whereas on-road trials yield tangible data offering a more technology-driven perspective on truck platooning.
Methodology
review
Sample size: 30
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-04 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Methodological Resource: tool software, validation psychometrics