Learning a Safe Driving Policy for Urban Area from Real-World Data
Bibliographic record
Abstract
Driving safely on the urban roads is a major impediment in achieving level 5 autonomy. To achieve this, two main streams of approaches have been proposed: module-based and end-to-end. Module based solutions try to solve the problem by dividing the whole task of driving into separate modules and solving each one at a time. On the other hand, end-to-end approaches try to provide the control command directly from the sensor data input, like what a human driver does. Deep reinforcement learning (DRL) is one of the algorithm families that has received much attention recently to achieve end-to-end solutions. As this approach is challenging, almost all the related works use simulator generated data for training a policy network. However, synthetic data does not capture the complexity, variability, realism, and diversity of the real-world environment. A reinforcement learning (RL) policy trained on synthetic dataset necessarily makes it unreliable in real-world deployment. In this study, we propose an actor-critic DRL model to learn a driving policy from a real-world urban driving dataset. The policy enables the RL agent to keep safe distance from the leading vehicle, follow traffic light, and prevents the agent from going off-road. To optimize the policy we use proximal policy optimization (PPO), a state-of-the-art reinforcement learning algorithm. Simulation results show that the agent learns some of the basic safe driving requirements effectively.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".