An Uncertainty-Aware, Dual-Tiered Decision-Making Method for Safe Autonomous Driving
Bibliographic record
Abstract
Learning-based algorithms play a pivotal role in various functional modules of an autonomous driving system. Recognizing and accounting for the impact of learning-based algorithm uncertainties on other functional modules can be crucial for making more dependable driving behavior decisions and for selecting more appropriate driving precaution measures, as opposed to directly executing safety fallback strategies like emergency braking. With the motivation of optimizing the safety without unnecessary disruption to the driving experience, this paper proposes an uncertainty-aware, dual-tiered decision making method named DBNID, which is based on dynamic Bayesian network (DBN) and influence diagram (ID). To begin, the paper formulates the effects of uncertainty propagation stemming from perception and prediction modules using a DBN model. The effects are then solved by an expectation maximum (EM) algorithm. Furthermore, how the uncertainty propagation effects are considered in the decision making process is then presented in an ID model with the introduction of the utility function formulation. Finally, the proposed DBNID method is evaluated on a simulation platform tailored for real-world autonomous driving testing. By considering uncertainty propagation, the results demonstrate that the proposed method can significantly reduce the likelihood of violating critical safe stop requirements, while simultaneously enhancing the minimum time-to-collision (TTC) performance. DBNID method offers valuable insights of integrating learning-based algorithm uncertainties into autonomous vehicle decision making process.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".