Evaluation of emergency driving behaviour and vehicle collision risk in connected vehicle environment: A deep learning approach
Bibliographic record
Abstract
Abstract In the latest connected vehicle (CV) message standards, including SAE J2735‐2016 and T‐CASE 53–2017, the basic safety messages (BSMs) are designed specifically as effective measures for traffic safety management and applications. In this study, a testbed on the Nanchang‐Jiujiang Intelligent Highway in Jiangxi, China is illustrated as an example, and the basic architecture and key technologies is introduced for a proactive traffic safety utilisation, where the core basic safety message (BSM) data are sorted and implemented to perceive and predict risky driving behaviours in a field environment. On this basis, an accurate insight into time‐critical driving safety issues can be achieved by investigating raw BSM data, such as the inter‐vehicle distance, driver manipulation, vehicle speed, and acceleration/deceleration. Furthermore, to effectively take advantage of connected vehicle information and perceive the high uncertainty of driving behaviours during an emergency situation and evaluate the driving safety in mixed traffic scenarios, a long short‐term memory (LSTM) based deep learning framework is introduced to build a multi‐horizon vehicle crash risk prediction model using continuous BSMs as the inputs. The experimental results demonstrate the significance of connected vehicle data and deep learning algorithms for improving driving safety and promoting widespread deployment and application of connected vehicles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".