Parallel Motion Planning: Learning a Deep Planning Model against Emergencies
Why this work is in the frame
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Bibliographic record
Abstract
To handle the issue of preventing emergencies for motion planning in autonomous driving, we present a novel parallel motion planning framework. Artificial traffic scenes are firstly constructed based on real traffic scenes. A deep planning model which can learn from both real and artificial scenes is developed and used to make planning decisions in an end-to-end mode. To prevent emergencies, a generative adversarial networks (GAN) model is designed and learns from the artificial emergencies from artificial traffic scenes. During deployment, the well-trained GAN model is used to generate multiple virtual emergencies based on the current real scene, and the well-trained planning model simultaneously makes different planning decisions for both virtual scenes and the current scenes. The final planning decision is made by comprehensively analyzing observations and virtual emergencies. Through parallel planning, the planner can timely make rational decision without a large number of calculations when an emergency occurs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it