Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning
Bibliographic record
Abstract
The present study proposes a framework for learning the car-following behavior of drivers based on maximum entropy deep inverse reinforcement learning. The proposed framework enables learning the reward function, which is represented by a fully connected neural network, from driving data, including the speed of the driver’s vehicle, the distance to the leading vehicle, and the relative speed. Data from two field tests with 42 drivers are used. After clustering the participants into aggressive and conservative groups, the car-following data were used to train the proposed model, a fully connected neural network model, and a recurrent neural network model. Adopting the fivefold cross-validation method, the proposed model was proved to have the lowest root mean squared percentage error and modified Hausdorff distance among the different models, exhibiting superior ability for reproducing drivers’ car-following behaviors. Moreover, the proposed model captured the characteristics of different driving styles during car-following scenarios. The learned rewards and strategies were consistent with the demonstrations of the two groups. Inverse reinforcement learning can serve as a new tool to explain and model driving behavior, providing references for the development of human-like autonomous driving models.
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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".