Stability and Intervehicle Distance Analysis of Vehicular Platoons: Highlighting the Impact of Bidirectional Communication Topologies
Bibliographic record
Abstract
Vehicular platooning, a configuration comprising a leading vehicle and multiple follower vehicles (FVs) seeks to achieve and maintain specific intervehicle distances (IDs) while synchronizing FVs with the velocity and acceleration of the leading vehicle. Before attaining a desired stable state, the IDs may undergo transient fluctuations. While the attainment of internal stability is pivotal for realizing the intended spacing between vehicles, it does not inherently guarantee that these transient fluctuations remain within safe thresholds, thereby mitigating the risk of collisions. Communication between vehicles has a critical role in vehicular platooning and significantly influences these transient distance fluctuations. Consequently, we present a mapping between the initial conditions and these transient fluctuations which hinges on the communication topology (CT), as well as the control parameters. Specifically, our focus is directed toward bidirectional CTs (BDCTs), wherein FVs possess the capability to communicate both with preceding and subsequent vehicles within the platoon. Investigation of these mappings illuminates the advantages and disadvantages of various BDCTs. Notably, we discern that within BDCTs, the receipt of information from a greater number of vehicles situated behind may at times hinder the overall performance of the platoon, resulting in larger deviations from the desired IDs or the velocity and acceleration of the leading vehicle. In contrast, information derived from vehicles located ahead, particularly the leading vehicle itself, serves to enhance IDs and therefore contributes significantly to the safety of the platoon. In conclusion, our theoretical insights are substantiated through a series of simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".