Resilient Neural-Sliding-Mode Distance Regulation in Merging Control of Heterogeneous Connected Automated Vehicles Subject to Deception Attacks
Bibliographic record
Abstract
Resilient distance regulation process is significantly essential in intelligent transportation systems with connected automated vehicles (CAVs), due to vulnerability of communication links within the vehicles of a platoon. On the other hand, time varying distance regulation process is usually ignored in adaptive cruise control procedures, which yields to be impossible for other vehicles on the right lane or the ramp to merge to the platoon. So, the main contribution of this paper is to design an adaptive resilient neural-sliding-mode cruise control with distance regulation procedure to make the following vehicles to track the leader's velocity and acceleration profile, while keeping a time-varying safe distance between each two consecutive vehicles, even when another vehicle seeks to merge into the platoon. To this end, at first an adaptive neural sliding mode control procedure along with a variable structure virtual disturbance observer is designed to provide a safe and smooth time-varying distance regulation process for the vehicle receiving the merging signal as well as maintaining a predefined distance between other two vehicles, while there exists cyber-attacks, external disturbances, and unknown nonlinearity in the system. Furthermore, it is assured that the followers track the leader's velocity and acceleration profile in a leader-to-all topology. To the authors’ knowledge, this is the first time that a resilient nonlinear cruise control procedure is proposed with distance regulation process for a platoon of nonlinear CAVs facing unknown nonlinearities and deception attacks. Finally, numerical results validate the effectiveness of the proposed procedure on achieving control objectives, resilience, and advantages compared to a recently proposed method.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".