Development Of Framework For In-Vehicle Rear-End Collision Warning System Considering Driver Characteristics
Bibliographic record
Abstract
Rear-end collisions are one of the serious traffic safety problems. These collisions occur when the following vehicle driver is inattentive or could not judge a potential rear-end collision situation. The use of rear-end collision warning systems (RECWS) may help drivers to avoid rear-end collision. The existing systems assumed constant driver reaction time for all driver population in their design and evaluation. They also ignore variations in driver characteristics, such as age and gender. The objectives of this thesis research are: (1) to develop reaction-time models that incorporate driver characteristics, (2) to develop a car-following simulation model that represents driver behaviour, and (3) to develop a rear-end collision warning system that accounts for driver characteristics and produces reliable collision warnings. In the human-factors study, four driver reaction-time models are developed for four different car-following scenarios: lead vehicle decelerating with normal deceleration rate, lead vehicle decelerating with emergency deceleration rate, lead vehicle stationary, and car-following acceleration regime. These models describe how the driver and situational factors affect reaction-time. The driver factors include age and gender, and the situational factors include speed and spacing between the following and lead vechiles. The developed car-following model assumes that drivers adjust their speeds based on information of both the lead and the back vehicles. The model also assumes that the driver reaction-time varies based on driver characteristics and kinematics. The proposed model represents driver behaviour in acceleration, deceleration, and steady state regimes of the car-following scenarios. Another unique feature of the model is that it explicitly considers information on the back vehicle. The model is calibrated and validated using vehicle tracking database. The driver reaction-time models and other kinematics constraints were integrated to develop a driver-sensitive rear-end collision warning system algorithm (RECWA). The developed car-following model is used to evaluate and validate the performance of the proposed RECWA. The results show that the proposed RECWA is functioning and producing reliable results. With further research and development, the proposed algorithm can be integrated into driving simulators or real vehicles to further evaluate and examine its benefits.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".