Development Of Framework For In-Vehicle Rear-End Collision Warning System Considering Driver Characteristics
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».