Two methods of time series analysis were applied to naturalistic driving data. The SAX method reduces the dimensionality of the data by discretizing and quantizing it into distinct symbols. The matrix profile method works on raw data and computes a Euclidian distance measure between subsequences of the time series. Both methods can be used to search for motifs and discords (anomalies) in the data. We discuss the applications of these methods to look for driving patterns and show an example of a left turn that was identified using both methods. After comparing the methods, the matrix profile was the preferred method
This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far fo...
Driving is a complex activity that requires multi-level skilled operations (e.g., acceleration, brak...
Driving behavior is considered as a unique driving habit of each driver and has a significant impact...
Silva, M. I., & Henriques, R. (2021). TripMD: Driving patterns investigation via motif analysis. Exp...
Driving style classification can be useful for various intelligent vehicle applications and can impr...
Advanced Driver Assistance Systems, or ADAS, which can notify the driver of potential dangers or eve...
Silva, M. I., & Henriques, R. (2020). Finding manoeuvre motifs in vehicle telematics. Accident Analy...
Traditional trajectory anomaly detection aims to find abnormal trajectory points or sequences using ...
Visualizing cyclical spatio-temporal data is an important part of understanding how and why objects ...
Abstract Driver behaviour profiling, specifically in relation to identifying `good' versus `bad' dr...
Our objective in this contribution is to categorize driver behavior in terms of preturning maneuvers...
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and...
Driver behavior heterogeneity is a significant aspect to understand the individual behavioral variat...
Driving is a common task that involves skill and individual preferences that can differ between driv...
This study explored the use of two types of advanced driver assistance systems (ADAS) as tools for ...
This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far fo...
Driving is a complex activity that requires multi-level skilled operations (e.g., acceleration, brak...
Driving behavior is considered as a unique driving habit of each driver and has a significant impact...
Silva, M. I., & Henriques, R. (2021). TripMD: Driving patterns investigation via motif analysis. Exp...
Driving style classification can be useful for various intelligent vehicle applications and can impr...
Advanced Driver Assistance Systems, or ADAS, which can notify the driver of potential dangers or eve...
Silva, M. I., & Henriques, R. (2020). Finding manoeuvre motifs in vehicle telematics. Accident Analy...
Traditional trajectory anomaly detection aims to find abnormal trajectory points or sequences using ...
Visualizing cyclical spatio-temporal data is an important part of understanding how and why objects ...
Abstract Driver behaviour profiling, specifically in relation to identifying `good' versus `bad' dr...
Our objective in this contribution is to categorize driver behavior in terms of preturning maneuvers...
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and...
Driver behavior heterogeneity is a significant aspect to understand the individual behavioral variat...
Driving is a common task that involves skill and individual preferences that can differ between driv...
This study explored the use of two types of advanced driver assistance systems (ADAS) as tools for ...
This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far fo...
Driving is a complex activity that requires multi-level skilled operations (e.g., acceleration, brak...
Driving behavior is considered as a unique driving habit of each driver and has a significant impact...