TY - GEN
T1 - Enhancing AV Safety
T2 - International Conference on Transportation and Development 2024: Transportation Safety and Emerging Technologies, ICTD 2024
AU - Channamallu, Sai Sneha
AU - Almaskati, Deema
AU - Kermanshachi, Sharareh
AU - Pamidimukkala, Apurva
N1 - Publisher Copyright:
© ASCE.
PY - 2024
Y1 - 2024
N2 - Safety is the predominant concern in the advancement of automated vehicles (AVs); therefore, extensive real-world testing is underway to ensure their secure operation. Despite the widespread belief that they will decrease the frequency of accidents, there remains uncertainty about their impact on the severity of crashes in which they are involved. The primary objective of this study is to use the bagging classifier technique to predict the likelihood of injuries in accidents involving AVs. This was accomplished by conducting an in-depth examination of a wide range of independent variables and an analysis of injuries sustained in crash incidents involving AVs from 2014 to July 2023. The bagging classifier model showed notable effectiveness, achieving a balanced accuracy of 0.59, along with high precision and recall values of 0.94 and 0.97, respectively. These metrics indicate the model’s strong capability for accurately identifying severe crash outcomes and minimizing false positives. The precision-recall curve and a modified F1 score of 2.39 further endorse the model’s performance, particularly highlighting its efficiency in handling the class imbalance present in the dataset. The validation and learning curves underscore the model’s optimal complexity, displaying its proficiency in identifying essential patterns without succumbing to overfitting. Collectively, these metrics underscore the model’s success in predicting injury outcomes in AV crashes with a high level of accuracy. This study contributes to the literature on AV safety by providing valuable insights for manufacturers and policymakers that will enable them to develop effective safety features and strategies, thereby enhancing traffic safety.
AB - Safety is the predominant concern in the advancement of automated vehicles (AVs); therefore, extensive real-world testing is underway to ensure their secure operation. Despite the widespread belief that they will decrease the frequency of accidents, there remains uncertainty about their impact on the severity of crashes in which they are involved. The primary objective of this study is to use the bagging classifier technique to predict the likelihood of injuries in accidents involving AVs. This was accomplished by conducting an in-depth examination of a wide range of independent variables and an analysis of injuries sustained in crash incidents involving AVs from 2014 to July 2023. The bagging classifier model showed notable effectiveness, achieving a balanced accuracy of 0.59, along with high precision and recall values of 0.94 and 0.97, respectively. These metrics indicate the model’s strong capability for accurately identifying severe crash outcomes and minimizing false positives. The precision-recall curve and a modified F1 score of 2.39 further endorse the model’s performance, particularly highlighting its efficiency in handling the class imbalance present in the dataset. The validation and learning curves underscore the model’s optimal complexity, displaying its proficiency in identifying essential patterns without succumbing to overfitting. Collectively, these metrics underscore the model’s success in predicting injury outcomes in AV crashes with a high level of accuracy. This study contributes to the literature on AV safety by providing valuable insights for manufacturers and policymakers that will enable them to develop effective safety features and strategies, thereby enhancing traffic safety.
UR - http://www.scopus.com/inward/record.url?scp=85197298201&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85197298201&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85197298201
T3 - International Conference on Transportation and Development 2024: Transportation Safety and Emerging Technologies - Selected Papers from the International Conference on Transportation and Development 2024
SP - 538
EP - 549
BT - International Conference on Transportation and Development 2024
A2 - Wei, Heng
PB - American Society of Civil Engineers (ASCE)
Y2 - 15 June 2024 through 18 June 2024
ER -