Uncertainty-Aware Decision-Making for Autonomous Driving at Uncontrolled Intersections
Why this work is in the frame
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Bibliographic record
Abstract
Reinforcement learning (RL) has been widely used in the decision-making of autonomous vehicles (AVs) in recent studies. However, existing RL methods generally find the optimal policy by maximizing the expectation of future returns, which lacks distributional treatments of risky situations. Additionally, various uncertainties arising from the environment could also cause unreliable decisions, particularly in some complex urban environments. In this paper, the fully parameterized quantile network (FPQN) is utilized to estimate the full return distribution. Then, the conditional value-at-risk (CVaR) is utilized with the return distribution information to generate uncertainty-aware driving behavior. Additionally, an uncontrolled four-way intersection is developed by the Simulation of Urban Mobility (SUMO) simulation platform, which considers both the surrounding vehicles (SVs) and pedestrians. More specifically, to simulate the real-world traffic environment, the uncertainty arising from the occlusion, and the behavior uncertainty of surrounding traffic participants are also considered. The experiment results suggest that the proposed method outperforms the baseline methods in terms of safety. Furthermore, the results also indicate that the proposed method can make reasonable decisions in some challenging driving cases in the presence of uncertainty.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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