Transformer-Based Sensor Fusion for Autonomous Vehicles: A Comprehensive Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sensor fusion is vital for many critical applications, including robotics, autonomous driving, aerospace, and beyond. Integrating data streams from different sensors enables us to overcome the intrinsic limitations of each sensor, providing more reliable measurements and reducing uncertainty. Moreover, deep learning-based sensor fusion unlocked the possibility of multimodal learning, which utilizes different sensor modalities to boost object detection. Yet, adverse weather conditions remain a significant challenge to the reliability of sensor fusion. However, introducing the Transformer deep learning model in sensor fusion presents a promising avenue for advancing its sensing capabilities, potentially overcoming that challenge. Transformer models proved powerful in modeling vision, language, and numerous other domains. However, these models suffer from high latency and heavy computation requirements. This paper aims to provide: 1) an extensive overview of sensor fusion and transformer models; 2) an in-depth survey of the state-of-the-art (SoTA) methods for Transformer-based sensor fusion, focusing on camera-LiDAR and camera-radar methods; and 3) a quantitative analysis of the SoTA methods, uncovering research gaps and stimulating future work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 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