Deep Learning Based Road Boundary Detection Using Camera and Automotive Radar
Bibliographic record
Abstract
Autonomous vehicles should be capable of operating in all types of weather conditions. Drivable road region detection is a core component of the perception stack of self-driving vehicles. Current approaches for detecting road regions perform well in good weather but lack in inclement weather conditions. In this paper, we examine the effect of inclement weather on the camera-based state-of-the-art deep learning approaches and introduce a new camera and automotive radar-based multimodal deep learning model to efficiently detect drivable road regions in all weather conditions. We also propose a novel approach to overcome the sparse resolution problem of automotive radars and a way to effectively use it in higher precision tasks such as image segmentation. To validate our work, we have augmented the nuScenes data with rain and fog to add challenging weather conditions. Experimental results show that the performance of the state-of-the-art techniques drops 18% in bad weather conditions while our proposed method improves the performance by 12% compared to the state-of-the-art.
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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 | 0.000 | 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 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".