Grant: Engage Plus Grants Program
HEC Montréal
$12,500.00
Lane-change detection
EF 2018-19
Résumé
Since the end of 2013, Intact Insurance has offered usage-based insurance programs designed to leverage*data-driven technologies and improve insurance products, by collecting driving data from customers using*either a GPS dongle connected to the vehicle's on-board computer or a smartphone application. In this context,*vehicle position and other sensor data, such as acceleration, wheel rotation, and magnetic azimuth, are recorded*when available. This data is used to characterize driving behavior and, in particular, to detect risky events such*as speeding, harsh breaking, hard acceleration, sharp turns or abrupt lane changes. Along with other contextual*information such as historical risk of a given road, road conditions, other cars' behaviour and speed limits,*these data have the potential to provide better customer segmentation along with risk and cost prediction,*which in turn could help Intact Insurance provide the most competitive pricing to their customers. In order to*build customer-specific road safety indicators and risk factors based on i) where and when the client drives*(contextual information), and ii) how the client drives (driving profile), complex automated procedures must be*developed. The main objective of this research is to tackle issues related to driver's behaviour profiling through*the development and validation of a measure for lane change detection.*Specifically, we propose in this project to investigate how the use of accelerometers and gyroscopes data*collected through the Intact smartphone applications ""my Driving Discount"" or ""automerit"" can improve lane*change detection. We also propose to evaluate the minimum data frequency required to achieve a reasonable*detection.
Programme
Engage Plus Grants Program
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
522430-2018
Name nom
Labbe, Aurélie
Department département
Sciences de la décision
Organization id
65
Institution établissement
HEC Montréal
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2018
Competition year année de concours
2018
Award amount
12500
Program id
EGP2
Program name en
Engage Plus Grants Program
Program name fr
Programme de subvention d'engagement partenarial Plus
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1552
Committee name en
Quebec Internal Decision Committee
Committee name fr
Comité de décision interne pour le Québec
Area of application code
700
Area of application group en
Transportation systems and services
Area of application group fr
Services et systèmes de transport
Area of application en
Transportation systems and services
Area of application fr
Services et systèmes de transport
Research subject code
3011
Research subject group en
Statistics and probability
Research subject group fr
Statistique et probabilité
Research subject en
Multivariate analysis
Research subject fr
Analyse multivariée
Partie
1
Nb partie
1
Application title
Lane-change detection
Keyword
NSERC
Application summary
Since the end of 2013, Intact Insurance has offered usage-based insurance programs designed to leverage*data-driven technologies and improve insurance products, by collecting driving data from customers using*either a GPS dongle connected to the vehicle's on-board computer or a smartphone application. In this context,*vehicle position and other sensor data, such as acceleration, wheel rotation, and magnetic azimuth, are recorded*when available. This data is used to characterize driving behavior and, in particular, to detect risky events such*as speeding, harsh breaking, hard acceleration, sharp turns or abrupt lane changes. Along with other contextual*information such as historical risk of a given road, road conditions, other cars' behaviour and speed limits,*these data have the potential to provide better customer segmentation along with risk and cost prediction,*which in turn could help Intact Insurance provide the most competitive pricing to their customers. In order to*build customer-specific road safety indicators and risk factors based on i) where and when the client drives*(contextual information), and ii) how the client drives (driving profile), complex automated procedures must be*developed. The main objective of this research is to tackle issues related to driver's behaviour profiling through*the development and validation of a measure for lane change detection.*Specifically, we propose in this project to investigate how the use of accelerometers and gyroscopes data*collected through the Intact smartphone applications ""my Driving Discount"" or ""automerit"" can improve lane*change detection. We also propose to evaluate the minimum data frequency required to achieve a reasonable*detection.