Dynamic Multiobjective Optimization of Lane‐Changing Trajectories Based on Reinforcement Learning
Bibliographic record
Abstract
To enhance the rationality of lane‐changing decisions and the adaptability of trajectory planning, this study incorporates short‐term driving styles to construct a multiobjective optimized lane‐changing trajectory planning model based on naturalistic driving data. First, lane‐changing behavior rules were defined to extract lane‐changing and lane‐keeping data. Essential factors influencing lane‐changing behavior were identified using the eXtreme Gradient Boosting (XGBoost) model. Based on the essential factors, drivers were classified into three categories (conservative, moderate, and aggressive) using Density‐Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which revealed the behavioral difference during lane‐changing. Subsequently, an attention‐enhanced long short‐term memory (LSTM) network was employed to predict surrounding vehicle trajectories, generating dynamically updated environmental parameters. Further considering comfort and safety benefits during lane‐changing, a multiobjective trajectory planning model was developed. Reinforcement learning algorithms iteratively optimized the trajectories to derive the optimal trajectory. Finally, the behavioral characteristics of planned trajectories for the three categories of drivers and the deviations between planned and actual trajectories were compared. Results indicate that planned trajectories exhibit shorter lane‐changing length and higher efficiency compared with actual trajectories. Planned trajectory can smooth microlevel behavior and improve safety and comfort during lane‐changing. For different types of drivers, conservative drivers show the longest lane‐changing length but smallest headway space distances, which reflects drivers’ caution during lane‐changing. Aggressive drivers mostly focus on speed improvement. The findings can be applied to vehicle trajectory planning in connected environments, which can enhance the lane‐changing efficiency while ensuing safety.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".