TY - JOUR
T1 - Effects of collision warning characteristics on driving behaviors and safety in connected vehicle environments
AU - Zhao, Wenjing
AU - Gong, Siyuan
AU - Zhao, Dezong
AU - Liu, Fenglin
AU - Sze, N. N.
AU - Huang, Helai
N1 - Funding Information:
This work was jointly supported by:1) the National Natural Science Foundation of China ( 71901038 ), 2) the National Key R&D Program of China ( 2019YFB1600100 ), 3) the Shaanxi Province Science Foundation ( 2020JQ-392 ), 4) the Research Funds for the Central Universities, Chang’an University ( 300102243201 ), 5) Research Committee of the Hong Kong Polytechnic University (H-ZJMQ), 6) the Smart Traffic Fund (PSRI/09/2108/PR).
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/6
Y1 - 2023/6
N2 - With the emerging connected vehicle (CV) technologies, a novel in-vehicle omni-direction collision warning system (OCWS) is developed. For example, vehicles approaching from different directions can be detected, and advanced collision warnings caused by vehicles approaching from different directions can be provided. Effectiveness of OCWS in reducing crash and injury related to forward, rear-end and lateral collision is recognized. However, it is rare that the effects of collision warning characteristics including collision types and warning types on micro-level driver behaviors and safety performance is assessed. In this study, variations in drivers’ responses among different collision types and between visual only and visual plus auditory warnings are examined. In addition, moderating effects by driver characteristics including drivers’ demographics, years of driving experience, and annual driving distance are also considered. An in-vehicle human–machine interface (HMI) that can provide both visual and auditory warnings for forward, rear-end, and lateral collisions is installed on an instrumented vehicle. 51 drivers participate in the field tests. Performance indicators including relative speed change, time taken to accelerate/decelerate, and maximum lateral displacement are adopted to reflect drivers’ responses to collision warnings. Then, generalized estimation equation (GEE) approach is applied to examine the effects of drivers’ characteristics, collision type, warning type and their interaction on the driving performance. Results indicate that age, year of driving experience, collision type, and warning type can affect the driving performance. Findings should be indicative to the optimal design of in-vehicle HMI and thresholds for the activation of collision warnings that can increase the drivers’ awareness to collision warnings from different directions. Also, implementation of HMI can be customized with respect to individual driver characteristics.
AB - With the emerging connected vehicle (CV) technologies, a novel in-vehicle omni-direction collision warning system (OCWS) is developed. For example, vehicles approaching from different directions can be detected, and advanced collision warnings caused by vehicles approaching from different directions can be provided. Effectiveness of OCWS in reducing crash and injury related to forward, rear-end and lateral collision is recognized. However, it is rare that the effects of collision warning characteristics including collision types and warning types on micro-level driver behaviors and safety performance is assessed. In this study, variations in drivers’ responses among different collision types and between visual only and visual plus auditory warnings are examined. In addition, moderating effects by driver characteristics including drivers’ demographics, years of driving experience, and annual driving distance are also considered. An in-vehicle human–machine interface (HMI) that can provide both visual and auditory warnings for forward, rear-end, and lateral collisions is installed on an instrumented vehicle. 51 drivers participate in the field tests. Performance indicators including relative speed change, time taken to accelerate/decelerate, and maximum lateral displacement are adopted to reflect drivers’ responses to collision warnings. Then, generalized estimation equation (GEE) approach is applied to examine the effects of drivers’ characteristics, collision type, warning type and their interaction on the driving performance. Results indicate that age, year of driving experience, collision type, and warning type can affect the driving performance. Findings should be indicative to the optimal design of in-vehicle HMI and thresholds for the activation of collision warnings that can increase the drivers’ awareness to collision warnings from different directions. Also, implementation of HMI can be customized with respect to individual driver characteristics.
KW - Collision warning system
KW - Connected vehicles
KW - Driving performance
KW - Field tests
KW - Human-machine interfaces
KW - Instrumented vehicles
UR - http://www.scopus.com/inward/record.url?scp=85151806344&partnerID=8YFLogxK
U2 - 10.1016/j.aap.2023.107053
DO - 10.1016/j.aap.2023.107053
M3 - Journal article
C2 - 37030178
AN - SCOPUS:85151806344
SN - 0001-4575
VL - 186
JO - Accident Analysis and Prevention
JF - Accident Analysis and Prevention
M1 - 107053
ER -
