Application of a modified Integrated Safety Chain using in-depth crash data to identify factors associated with serious injury crashes: a method to prioritise currently available active safety systems and to identify new opportunities to advance vehicle safety.
Creators: name: Fitzharris, Michael
name: Lenné, Mike
name: Liu, Sara
name: Pok Arundell, Tandy
name: Peiris, Anne
name: Tingvall, Claes
name: Corben, Bruce
name: Bowman, Diana
name: Morris, Andrew
Publisher: name: Monash University
Year: 2023
Subjects: Injury prevention
Automotive safety engineering
Road transportation and freight services

description: Recognising the ambition of Vision Zero, vehicle safety will play a critical role in reducing the number of road users seriously injured globally. The objective of this research, therefore, was to identify currently available and required future countermeasures that will lead to the elimination of serious injury. To meet this objective a systematic approach to the analysis of in-depth crash data using case-by-case analysis linking contributing factors to safety countermeasures was developed. In-depth crash investigation data collected as part of the MUARC-TAC Enhanced Crash Investigation Study (ECIS) was used. 400 drivers (MAIS 3+: 47%) admitted to a major trauma centre in Victoria, Australia, were included. Data sources included: driver or next-of-kin/family interview, ambulance and medical records, and police data. Vehicle and scene analysis was undertaken. Crashes were reconstructed using HVE and PC-Crash. EDR data was accessed where available. Using a modified version of Tingvall’s Integrated Safety Chain, contributing factors and safety countermeasures across the 10-phase crash chain were examined using a case-by-case approach. Contributing factors were those associated with crash occurrence and injury severity. An countermeasure library was established with each of the 278 countermeasures linked to a specific contributing factor. Countermeasures included those focussed on the driver, passive and active vehicle safety systems, road infrastructure and post-crash response. The efficacy and time-horizon of each was assessed and estimated for future active safety systems. All applicable countermeasures for each crash and injured driver were identified; these were considered to be sensitive to the countermeasure effect. Driver distraction (48.8%), sudden sickness (10.0%), drowsy driving (24.5%), and impaired driving (19.8%) resulted in lane departure and cross-path vehicle movements; this, combined with low proportion of driver pre-crash braking (55%, 1.3 s) and exceeding the speed limit (27.0%) demonstrates the need for intervening safety systems (e.g., ISA, AEB). Intervening systems to correct lane deviations and intersection entry are also required. The findings highlight the importance of in-depth data in establishing the use case for existing but relatively new systems as well as the identification of system capability limits in addressing current crash scenarios. These crash scenarios represent development opportunities for new standalone active safety systems. However, for full safety benefits to be realised, and to address the full range of driver performance and impairments, next generation systems that are fully integrated with one another are required (e.g., AEB + driver monitoring systems, DMS). Occupant status monitoring, on-board sensors, V2I and V2V enabled technologies linked to chassis control systems will be central to the future safety architecture of the vehicle. The findings are relevant to passenger vehicle crashes where at least one driver was seriously injured and admitted to hospital. Other limitations associated with the sample and data collection methods must also be considered. The analysis method represents a powerful approach to analyse in-depth crash data and to understand crash causation, injury occurrence and applicable countermeasures. Adoption of this method using other datasets is recommended so that the full range of countermeasure needs across jurisdictions and other road user groups can be understood.