Longitudinal and lateral control methods from single vehicle to autonomous platoon
Bibliographic record
Abstract
To successfully implement the platoon control of connected and automated vehicles, it is necessary to address motion control issues to achieve longitudinal and lateral collaborative control. However, due to traffic capacity limitations and the complex traffic environment in which autonomous and human-driven vehicles coexist, autonomous platoon faces significant risks and challenges. This paper investigates longitudinal and lateral control issues from the perspective of a single vehicle up to a platoon, simulating the performance and suitability of various controllers. First, a longitudinal controller based on fuzzy logic and PID control is employed for speed tracking control of a single vehicle, followed by the adoption of an MPC controller based on the vehicle kinematics model to realize the lateral motion of a single vehicle. Second, the communication methods of the autonomous platoon are discussed, and the longitudinal controller that considers the platoon's various communication topologies is developed. Thirdly, a framework for robust integrated motion control is established, which combines the robust H-infinity longitudinal controller and the APF-based MPC lateral controller. Simulation results validate the effectiveness of the aforementioned controllers and reveal their limitations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".