Aggressive Driver Behavior Detection using Parallel Convolutional Neural Networks on Simulated and Real Driving Data
Bibliographic record
Abstract
The novel method proposed in this paper is compromised of application of two Convolutional Neural Networks (CNN) working in parallel to simultaneously classify driver behaviors while classifying maneuvers by using time series data. We claim that the Parallel Convolutional Neural Network (PCNN) not only speeds-up training time but also increases performance since having information about the maneuver helps to improve behavior classification performance and vice versa. In this study, both simulation and real-world driving datasets are utilized for driver behavior analysis. As simulation data, mobile phone sensor data are simulated as a time series using a combination of a traffic simulator (SUMO) and a car simulation system (Webots). The same type of data is collected with a specially designed vehicle traveled on a defined route around a predefined region. The collected data are then separately utilized as training and testing data for classification of both maneuvers (e.g turns and lane changes) and driver behaviors (e.g aggressive, non-aggressive) applying a novel method using deep learning on time series data. In addition, other methods which are commonly used for time series analysis, Hidden Markov Models(HMMs) and Recurrent Neural Networks (RNN), are applied to the same datasets to compare with PCNN. According to the results, the CNN classifiers perform efficiently for a single task and PCNN outperforms both single task-CNN and RNN with an average accuracy of 86%.
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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".