A Reinforcement Learning Based Approach for Controlling Autonomous Vehicles in Complex Scenarios
Bibliographic record
Abstract
Autonomous driving has gained an increased interest in both academia and industry, as autonomous vehicles (AVs) significantly improve road safety by reducing traffic accidents and human injuries. Motion control remains one of the main functions of Autonomous Vehicles, which generates the steering angle and velocity of the vehicle. While traditional Machine Learning techniques have been extensively used in the past to improve motion control in AVs, the attention has been recently drawn to the use of Deep Learning (DL) and Deep Reinforcement Learning (DRL) techniques. These techniques have been applied to improve motion control of AVs and to help them learn from their environment. However, existing works are limited to dealing with simple scenarios without taking into consideration other road participants (e.g., other vehicles, pedestrians, cyclists, and motorcycles). In this paper, we propose a DRL-based model using Deep-Q Networks to control the AV in a complex scenario with dense traffic involving road participants. The AV learns the policy of different actions to reach its destination in an intersection without accidents. We tested and validated our proposed approach using the CARLA simulator. The obtained results demonstrated the efficiency of our solution by achieving better learning in terms of travel delay and avoiding collisions.
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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 | 0.000 | 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".