Distributed Cooperative Control and Robust Optimization for Nonlinear Connected Automated Vehicles With Unknown Reaction Time Delays and Jerk Dynamics
Bibliographic record
Abstract
In complex traffic environments, the driving performance of the leader vehicle in a platoon can be greatly impacted by sudden and unexpected changes in vehicle acceleration rates. This phenomenon is known as unknown jerk dynamics (JDs), and it can lead to more extreme car-following behaviors (CFBs) in platoon tracking control, which may raise safety and traffic capacity issues. To tackle these concerns, this work studies cooperative platoon tracking control and intermittent optimization problems for connected autonomous vehicles (CAVs) with unknown reaction time delays (RTDs) using a nonlinear car following model (NCFM). In a free-design but directed communication network, we assume that the leader CAV’s external inputs have unknown but bounded parameters both for the JDs and RTDs, while only a small number of nearby follower CAVs are aware of the leader CAV’s acceleration signals. To solve these issues, we consider that each follower CAV implements a distributed observer law, which provides a reference signal stated as an estimated JD of the leader CAV. Then, a distributed platoon tracking control protocol is proposed to construct cooperative tracking controllers with identical inter-vehicle constraints (ICs). This maintains the desired safety distance between the CAVs and allows each follower CAV to track its leader CAV only through local information exchange. In addition, we present a robust intermittent optimization design and a novel intermittent sampling condition that can guarantee optimally scheduled feedback gains for the cooperative platoon tracking controllers to minimize the control cost in the presence of unknown JDs and RTDs under non-identical ICs. Simulation case studies are conducted to demonstrate the effectiveness of the proposed approaches. We also demonstrate the efficient development of such a distributed cooperative car-following model for the platoon’s motion (or as an intelligent speed advising system for automated or human-driven vehicles), resulting in a trip that is safe, comfortable, and energy efficient.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".