Multitarget Vehicle Tracking and Motion State Estimation Using a Novel Driving Environment Perception System of Intelligent Vehicles
Bibliographic record
Abstract
The multitarget vehicle tracking and motion state estimation are crucial for controlling the host vehicle accurately and preventing collisions. However, current multitarget tracking methods are inconvenient to deal with multivehicle issues due to the dynamically complex driving environment. Driving environment perception systems, as an indispensable component of intelligent vehicles, have the potential to solve this problem from the perspective of image processing. Thus, this study proposes a novel driving environment perception system of intelligent vehicles by using deep learning methods to track multitarget vehicles and estimate their motion states. Firstly, a panoramic segmentation neural network that supports end-to-end training is designed and implemented, which is composed of semantic segmentation and instance segmentation. A depth calculation model of the driving environment is established by adding a depth estimation branch to the feature extraction and fusion module of the panoramic segmentation network. These deep neural networks are trained and tested in the Mapillary Vistas Dataset and the Cityscapes Dataset, and the results showed that these methods performed well with high recognition accuracy. Then, Kalman filtering and Hungarian algorithm are used for the multitarget vehicle tracking and motion state estimation. The effectiveness of this method is tested by a simulation experiment, and results showed that the relative relation (i.e., relative speed and distance) between multiple vehicles can be estimated accurately. The findings of this study can contribute to the development of intelligent vehicles to alert drivers to possible danger, assist drivers’ decision-making, and improve traffic safety.
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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".