Distributed Cooperative Control and Robust Optimization for Nonlinear Connected Automated Vehicles With Unknown Reaction Time Delays and Jerk Dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it