Modelling motorized and non-motorized vehicle conflicts using multiagent inverse reinforcement learning approach
Bibliographic record
Abstract
Microsimulation models are effective for analysing road users’ interaction behaviour and assessing different facilities’ performance. However, only a few studies have developed simulation models for studying motorized and non-motorized vehicles conflicts. This is likely due to mixed traffic’s complexity and heterogeneity and the difficulty in accurately capturing road users’ avoidance maneuver. This study aims to adopt a multiagent simulation model to replicate road users’ microscopic behaviour and collision avoidance mechanisms in traffic conflict scenarios. Road users’ reward functions are recovered by the multiagent inverse reinforcement learning approach. The multiagent Actor-Critic deep learning algorithm is used to predict road users’ evasive action and assess their optimal policies. The findings demonstrate that the multiagent simulation model provides highly accurate predictions of road users’ trajectories and collision avoidance strategies. Furthermore, the results demonstrate a strong correlation between the predicted traffic conflict indicator from the simulated trajectories and that from the actual trajectories.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".