An Intelligent Emotional Real-Time Learning Controller for High-Speed Lane Keeping Assist System in Autonomous Vehicles<sup>*</sup>
Bibliographic record
Abstract
This article presents a novel intelligent control algorithm for the Lane Keeping Assist System based on Emotional Learning. The proposed controller can be trained online using the continuous stress signal generated by the critic unit during the control process. Fast learning convergence, stable behaviour, and high adaptability and robustness against disturbances are the main advantages of this controller when compared to other controllers such as PID, LQR, and MPC. The performance of the controller is validated in a high-speed lane following task on a winding road. It is shown the proposed controller performed better in both initial settlement and disturbance rejections with up to 50% less tracking error in the presence of high lateral wind force. Stability and adaptability to the vehicle’s dynamics are evaluated with different velocities and surface friction which ended up with 30% less tracking error in lateral position for slippery surfaces (25% less friction). Moreover, the computation cost is 80% lower compared to MPC. In contrast to other reinforcement learning algorithms, the proposed controller does not require a high training effort which makes it suitable for use in real-time applications.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".