Uncertainty-Aware Lane Change Prediction for Autonomous Driving Using LSTM Networks
Bibliographic record
Abstract
This paper addresses the challenge of accurately predicting lane change maneuvers in autonomous driving while quantifying prediction uncertainty. We present an LSTM-based model that predicts three classes: lane keeping, left lane change, and right lane change. By considering the position, velocity, and acceleration of multiple vehicles, we enhance the accuracy of our predictions, achieving an overall accuracy of 97.10% with our base LSTM model. Our analysis of different uncertainty sources, including Gaussian noise injection, Dropout noise, and deep ensembles, reveals varying levels of uncertainty, with mean standard deviations ranging from 0.0084 to 0.1094. This highlights the model’s sensitivity to different types of perturbations and the importance of uncertainty quantification for robust lane change prediction. This contribution enables more informed decisionmaking in ADAS and paves the way for safer autonomous driving.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".