Estimation of Steering Angle and Collision Avoidance for Automated Driving Using Deep Mixture of Experts
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a monocular camera-based method is proposed to estimate the steering angle in autonomous driving. A second-order particle filtering algorithm is used to estimate the steering angles. The filtering algorithm is modeled at the scene-level for varying driving patterns. For a given road scene, individual proposal and likelihood distributions are modeled with deep learning-based regression frameworks for normal driving and obstacle avoidance driving patterns, respectively, the proposal distribution is modeled using a novel long short-term memory-based mixture-of-expert; and the likelihood is modeled using a convolutional neural network. To estimate the driving pattern captured from the monocular camera, a long recurrent convolutional network is adopted and trained. By modeling the distribution at the scene-level for different driving patterns, we accurately model the particle filter distributions. Consequently, for autonomous driving, the steering angle is robustly estimated with few particles. The proposed framework is validated on multiple acquired sequences. A detailed comparative and parametric analysis of the algorithm is performed. The experimental results demonstrate the robustness and accuracy of our filtering algorithm for varying road scenes and driving behaviors.
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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 | 0.000 | 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 it