CMHT autonomous dataset: A multi-sensor dataset including radar and IR for autonomous driving
Bibliographic record
Abstract
Standardized datasets are essential for the development and evaluation of autonomous driving algorithms. As the types of sensors available to researchers increase, datasets containing a variety of temporally and spatially aligned sensors have become increasingly valuable. This paper presents a driving dataset recorded using a complete sensor suite for research on autonomous driving, perception, and sensor fusion. The dataset consists of over 9000 frames of data recorded at 10-20Hz using a complete sensor suite made up of Velodyne LiDAR, GPS/IMU, mm-wave radar, as well as color and infrared cameras. The capture scenarios include poor weather/lighting conditions, such as rain/night scenarios, and diverse traffic conditions, such as highways and cities with various objects. Both fully synchronized data and raw recordings in the form of ROS2 bags are provided, as well as 3D tracklet labels for individual objects. This paper provides technical details on the driving platform, data format, and utilities.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".