Learning Adaptive Cruise Control for Autonomous Vehicles Using End-to-End Deep Reinforcement Learning
Bibliographic record
Abstract
The most challenging task for autonomous vehicles (AVs) is to share the road with human-driven vehicles (HDVs), since the driving behaviors of HDVs are unknown to the AVs. And AVs are supposed to make optimal decisions in real time based on onboard sensors only. To achieve this goal, we model the problem as a Partially Observable Markov Decision Process (POMDP) and propose an end-to-end decision-making framework for AVs based on a deep reinforcement learning (DRL) algorithm in combination with classical control methods to allow vehicles to achieve an adaptive cruise control. To reduce the gap in the Sim2real problem, a high-fidelity simulator is developed using ROS-Gazebo, which allows for a realistic multi-vehicle simulation with various sensors. The raw data obtained from these sensors is the input of a LSTM neural network, which could be mapped directly to the low-level commands. Then, an adaptive driving policy will be learned automatically from the virtual environment through self-play training mode. Finally, the trained model is tested in both virtual platform and corresponding real-world scenario, which validates the effectiveness and feasibility of the proposed decision-making framework.
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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 | 0.001 |
| 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 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".