Decision-Making of an Autonomous Vehicle when Approached by an Emergency Vehicle using Deep Reinforcement Learning
Bibliographic record
Abstract
Autonomous Vehicles (AVs) are the future of road transportation which can increase safety, efficiency, and productivity. Decision-making of AVs in a highway environment with different goals like overtaking, staying in a lane, and merging have been the focus of many studies. In this study, we want to address a new edge case in autonomous driving when the AV (ego) needs to make the best lateral and longitudinal decisions when approached by an emergency vehicle (emg). To achieve the desired behavior and learn the sequence decision process, we trained ego with the help of Deep Reinforcement Learning (DRL) algorithms and compared the results with rule-based algorithms. We proposed two neural networks as function approximators that help the ego to learn the optimum actions. The driving environment for this problem was developed by using Simulation Urban Mobility (SUMO) as an open-source traffic simulator. We will show our proposed solution based on the DRL outperforming the rule-based solution and demonstrate that it has a decent performance both in normal driving situations and when an emergency vehicle is approaching.
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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 | 0.000 |
| 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".