Prediction of Car-Following Behavior of Autonomous Vehicle and Human-Driven Vehicle Based on Drivers’ Memory and Cooperation With Lead Vehicle
Bibliographic record
Abstract
Autonomous vehicles (AVs) have moved from hype to reality as the penetration and acceptance rate continues to increase. As they are slowly integrated into traffic with human-driven vehicles (HDVs), it is necessary to predict the car-following behaviors of AVs and HDVs for better control of AV–HDV mixed traffic. This study extends a data-driven car-following model to incorporate drivers’ memory, and cooperation with the lead vehicle. The model predicts the following vehicle’s speed in AV–HDV mixed traffic. The effect of drivers’ cooperation on car-following behavior was modeled using prospect theory (PT), whereas the driver’s memory was incorporated using the memory cell of a long short-term memory (LSTM) neural network. This extended car-following model is called the “PT-LSTM model.” Real-world vehicle trajectories of HDVs and AVs in the Waymo AV Open Dataset were used to calibrate and validate the PT-LSTM model. The PT-LSTM model demonstrated higher accuracy compared with the LSTM model that did not consider drivers’ cooperation, the multiple layer perceptron model, Gipps’ model, and the intelligent driver model that incorporated PT. The importance of variables in different time steps in the PT-LSTM model was also evaluated using SHapley Additive exPlanations (SHAP). The SHAP results showed that AV followers were more likely to cooperate with the lead HDV, whereas HDV followers were more likely to cooperate with the lead AV than the lead HDV. Thus, this study underscores the importance of considering drivers’ memory and cooperation with the lead vehicle for the prediction of car-following behaviors in AV–HDV mixed traffic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".