Decoding Passenger’s Brain Signals to Detect and Analyze Emergency Road Events
Bibliographic record
Abstract
With an increasing number of vehicles with automated functions on the road, road safety is still one of the biggest concerns for autonomous vehicles.Multiple crash reporting for vehicles shows the limitations of sensors and algorithms on vehicles equipped with Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS).The most important reason for these accidents is that there are function decencies in robustness, generalization, interpretability, logical completeness, etc., those function decencies may cause the safety of the intended functionality (SOTIF) accident under a triggering condition.Introducing a new type of sensor may achieve the goal of producing more reliable information with less uncertainty.In this thesis, passengers' brain signals, including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), were analyzed to extract road information to potentially prevent car accidents and provide public trust in high-level autonomous vehicles.For the EEG part, eventrelated potential (ERP) and machine learning techniques were used to analyze and classify the signals of two road events: pedestrians standing on the curb and suddenly crossing the street.Results show that the responses are 454 ± 234 ms before the reaction, and the average recognition accuracy of the regularized linear discriminant analysis (RLDA) classifier reached 95.81%.For the fNIRS part, a quantification method, which is based on cerebral oxygen exchange in the prefrontal cortex of passengers and a risk field is introduced.We also verified our findings in a real-car automatic emergency braking (AEB) and cut-in experiment performed at China Automotive Engineering Research Institute (CAERI) automobile testing base in Dazu, China.Overall, the results illustrate that EEG-based human-centric assistant driving systems have the potential of being iii deployed in high-level autonomous vehicles to enhance the safety of passengers and overall public safety.Chapter 5: Results.....
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".