Drivable Area Detection Using Deep Learning Models for Autonomous Driving
Bibliographic record
Abstract
Drivable area or free space detection is an important task in Advanced Driver-Assistance Systems (ADAS) and autonomous driving system. It can help intelligent vehicles understand road conditions and determine safe driving area. Semantic segmentation is a pixel-wise prediction which can classify each pixel into its category. In this paper, we propose a deep learning-based semantic segmentation architecture to predict the drivable area in front of the vehicle. Our model is built based on ResNet backbone with the Feature Pyramid Network (FPN) and Atrous Spatial Pyramid Pooling (ASPP) modules. The backbone in the bottom-up architecture extracts features and an ASPP is attached to the last decoder layer. Additionally, a top-down architecture with lateral connections is added in the decoder and the FPN utilizes the multi-scale features for final prediction. Our model is evaluated on the Cityscapes street scene dataset and achieves 95.90% mIoU on road segmentation. Next, the model is evaluated on the BDD100K large-scale diverse driving dataset with direct drivable region and alternative drivable region annotations. For this dataset our model achieves 84.58% mIoU which is comparable to some State-of-the-Art models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".