Highway Decision-Making and Motion Planning for Autonomous Driving via Soft Actor-Critic
Bibliographic record
Abstract
In this study, a decision-making and motion planning controller with continuous action space is constructed in the highway driving scenario based on deep reinforcement learning. In the decision-making and planning problem, the goal is to achieve the safety, efficiency, and comfort of automated vehicles. In the driving scenario, the surrounding vehicles are controlled by the intelligent driver model and a general model (minimizing overall braking induced by lane change, MOBIL), which enables them to react to the environment and mimic the vehicle interactions on the highway. Given the uncertainties in the driving conditions, a specific deep reinforcement learning technique, called soft actor-critic, is used to solve the decision-making and planning problem with continuous action space. Simulation results show that the proposed method can solve the decision-making and motion planning problem in the interactive traffic environment to carry out safe lane-change maneuvers and cruise at high speed. In addition, two control policies are developed with different weights on safety, efficiency, and comfort.
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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 | 0.001 | 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".