The role of off-path glances: A quantitative analysis of rear-end conflicts involving Chinese professional truck drivers as the striking partners
Artikel i vetenskaplig tidskrift, 2020
© 2020 National Safety Council and Elsevier Ltd Introduction: Rear-end crashes are one of the most frequent crash types in China, leading to significant economic and societal losses. The development of active safety systems – such as Automatic Emergency Braking System (AEBS) – could avoid or mitigate the consequences of these crashes in Chinese traffic situations. However, a clear understanding of the crash causation mechanisms is necessary for the design of these systems. Method: Manually coded variables were extracted from a naturalistic driving study conducted with commercial vehicles in Shanghai. Quantitative analyses of rear-end crashes and near crashes (CNC) were conducted to assess the prevalence, duration, and location of drivers’ off-path glances, the influence of lead vehicle brake lights on drivers’ last off-path glance, and driver brake onset, and the influence of off-path glances and kinematic criticality on drivers’ response to conflicts. Results: The results indicate that the Chinese truck drivers in our study rarely engage in distracting activities involving a phone or other handheld objects while driving. Instead, they direct their off-path glances mainly toward the mirrors, and the duration of off-path glances leading to critical situations are shorter compared to earlier analyses performed in Western countries. The drivers also often keep small margins. Conclusions: Overall, the combination of short time headway with off-path glances directed toward the mirror originates visual mismatches which, associated to a rapid change in the kinematic situation, cause the occurrence of rear-end CNC. When drivers look back toward the road after an off-path glance, a fast response seems to be triggered by lower values of looming compared to previous studies, possibly because of the short time headways. Practical Application: The results have practical implications for the development of driver models, for the design of active safety systems and automated driving, and for the design of campaigns promoting safe driving.
Naturalistic driving data
Active safety systems