Using Reinforcement Learning to Handle the Unintended Lateral Attack in the Intelligent Connected Vehicle Environment
Bibliographic record
Abstract
It is widely accepted that an unintended lateral attack is inevitable in the intelligent connected vehicle environment. This paper explores the feasibility of a reinforcement learning method named PPO (Proximal Policy Optimization) to handle the unintended lateral attack and keep the vehicle stay in the ego lane. Based on the China highway design guide, the discrete speed variants of 120 km/h, 100 km/h, and 80 km/h were selected, along with different curvatures ranging from 250 m to 1200 m in every 50 m as combinations of speed-curvature test. The tests were implemented in the Open.ai CarRacing-v0 simulation environment with an external racing wheel attached to simulate the unintended lateral attack. The simulation results show that the PPO can handle the unintended lateral attack on the standard-designed highway in China. The results can be applied to the intelligent connected vehicle to be mass-produced in the future.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".