Extracting Situations with Uneasy Driving in NDS-Data Volvo Cars

Format:

Conference/Event

Author/Creator:

Karlsson, Karlsson, author.

Contributor:

Jakobsson, Lotta

Kovaceva, Jordanka

Lindman, Magdalena

Svanberg, Bo

Wiberg, Henrik

Conference Name:

SAE 2014 World Congress & Exhibition (2014-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2014

Summary:

Different types of driver workload are suggested to impact driving performance. Operating a vehicle in a situation where the driver feel uneasy is one example of driver workload. In this study, passenger car driving data collected with Naturalistic Driving Study (NDS) data acquisition equipment was analyzed, aiming to identify situations corresponding to a high driver's subjective rating of unease'. Data from an experimental study with subjects driving a passenger car in normal traffic was used. Situations were rated by the subjects according to experienced unease', and the Controller Area Network (CAN) data from the vehicle was used to describe the driving conditions and identify driving patterns corresponding to the situations rated as uneasy'. These driving patterns were matched with the data in a NDS database and the method was validated using video data.Two data mining approaches were applied. The first was based on an ensemble classifier on general variables derived from the CAN-data to predict the subjective rating of segments of the data. The second used hierarchical clustering with a distance metric based on the principal variance components over segments. The ensemble classifier explained a large proportion of the variance when adjusting for driver and route. The hierarchical clustering method performed well, distinct clusters corresponding to a high driver subjective rating could be obtained.Identifying situations with increased driver workload in NDS data is a complex task addressing a large variation of traffic situations and driver experiences. The proposed method is a first approach to help address this topic using data mining

Notes:

Vendor supplied data

Publisher Number:

2014-01-0450

Access Restriction:

Restricted for use by site license