A Behavior Decision Method Based on Reinforcement Learning for Autonomous Driving
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
Autonomous driving vehicles can reduce congestion and improve safety while increasing traffic efficiency. To reflect the quality of driving more comprehensively, the driving safety, efficiency, and occupant comfort should be jointly optimized for autonomous vehicles. Furthermore, in order to cope with complicated traffic environments and achieve satisfactory driving performance, a powerful behavior decision-making module is indispensable for autonomous vehicles. Toward this end, we study a reinforcement-learning (RL)-based method to intelligently make the behavior decision in this article. A Markov decision process (MDP) model is first formulated with a comprehensive reward function, including the effects of driving safety, efficiency, and comfort. The knowledge of the surrounding vehicles is also leveraged to exploit the behavior prediction of the target vehicle. We then propose a behavior decision strategy based on the actor–critic (AC) mechanism, which can efficiently learn both a Gaussian policy function and a linear value function. Finally, the real traffic data are used to build up the simulations for evaluating the performances of the proposed method thoroughly. Simulation results show that our proposed method can significantly reduce the collision rate for autonomous vehicles.
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 | 0.001 | 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 | 0.001 |
| 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