Effects of collision warning characteristics on driving behaviors and safety in connected vehicle environments

Zhao, W., Gong, S., Zhao, D. , Liu, F., Sze, N.N. and Huang, H. (2023) Effects of collision warning characteristics on driving behaviors and safety in connected vehicle environments. Accident Analysis and Prevention, 186, 107053. (doi: 10.1016/j.aap.2023.107053)

Abstract

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.

• Item Type: Articles
• Additional 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 (300102240301), 5) the Engineering and Physical Sciences Research Council of the U.K. under the EPSRC Innovation Fellowship Grant EP/S001956/1, 6) the U.K. Royal Society-Newton Advanced Fellowship Grant NAF\R1\201213, 7) Research Committee of the Hong Kong Polytechnic University (H-ZJMQ), 8)the Smart Traffic Fund (PSRI/09/2108/PR).
• Keywords: Connected vehicles, collision warning system, human-machine interfaces, instrumented vehicles, field tests, driving performance.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Dezong
• Authors: Zhao, W., Gong, S., Zhao, D., Liu, F., Sze, N.N., and Huang, H.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: Accident Analysis and Prevention
• Publisher: Elsevier
• ISSN: 0001-4575
• ISSN (Online): 1879-2057
• Published Online: 06 April 2023
• Copyright Holders: Copyright © 2023 Elsevier Ltd
• First Published: First published in Accident Analysis and Prevention 186:107053
• Publisher Policy: Reproduced in accordance with publisher policy