TY - JOUR
T1 - Curve speed model for driver assistance based on driving style classification
AU - Chu, Duanfeng
AU - Deng, Zejian
AU - He, Yi
AU - Wu, Chaozhong
AU - Sun, Chuan
AU - Lu, Zhenji
PY - 2017
Y1 - 2017
N2 - Inappropriate speed in negotiating curves is the primary cause of rollovers and sideslips. In this study, the authors proposed an improved curve speed model considering driving styles, as well as vehicle and road factors. On the basis of a vehicle-road interaction model, the driver behaviour factor was introduced to quantify driving styles of curve speed choices. Firstly, the fuzzy synthetic evaluation method was utilised to classify the driving styles of 30 professional drivers into three different types (i.e. cautious, moderate and aggressive). Secondly, the classification results using fuzzy synthetic evaluation were compared to and verified with the K-means clustering method resulting over 60% the similarities. Finally, the proposed curve speed model was built and compared with four existing models. The authors' model has the following promising advantages: (i) it reflects the speed preferences of three different types of drivers on the premise of driving safety on curves; and (ii) it shows a stationary speed transition when the road adhesion coefficient exceeds 0.8, which indicates that rollover, instead of sideslip, becomes the primary cause for lateral instability crashes on curves. Therefore, this proposed curve speed model could be applied in a curve speed warning system to improve both driving safety and comfort.
AB - Inappropriate speed in negotiating curves is the primary cause of rollovers and sideslips. In this study, the authors proposed an improved curve speed model considering driving styles, as well as vehicle and road factors. On the basis of a vehicle-road interaction model, the driver behaviour factor was introduced to quantify driving styles of curve speed choices. Firstly, the fuzzy synthetic evaluation method was utilised to classify the driving styles of 30 professional drivers into three different types (i.e. cautious, moderate and aggressive). Secondly, the classification results using fuzzy synthetic evaluation were compared to and verified with the K-means clustering method resulting over 60% the similarities. Finally, the proposed curve speed model was built and compared with four existing models. The authors' model has the following promising advantages: (i) it reflects the speed preferences of three different types of drivers on the premise of driving safety on curves; and (ii) it shows a stationary speed transition when the road adhesion coefficient exceeds 0.8, which indicates that rollover, instead of sideslip, becomes the primary cause for lateral instability crashes on curves. Therefore, this proposed curve speed model could be applied in a curve speed warning system to improve both driving safety and comfort.
KW - alarm systems
KW - behavioural sciences
KW - driver information systems
KW - pattern classification
KW - pattern clustering
KW - road safety
UR - http://www.scopus.com/inward/record.url?scp=85030856391&partnerID=8YFLogxK
U2 - 10.1049/iet-its.2016.0294
DO - 10.1049/iet-its.2016.0294
M3 - Article
AN - SCOPUS:85030856391
SN - 1751-956X
VL - 11
SP - 501
EP - 510
JO - IET Intelligent Transport Systems
JF - IET Intelligent Transport Systems
IS - 8
ER -