In Europe, the deployment characteristics of frontal crash restraints are generally optimised to best protect an average young male, since a 50th percentile male dummy is used in a stylised frontal impact scenario. These single point restraint systems may not provide similar levels of effectiveness when the crash scenarios vary with respect to the regulatory and consumer crash test procedures. Previous research has demonstrated that varying restraint deployment characteristics according to occupant and crash variation can provide further injury reduction in frontal impacts. This thesis reports the investigation conducted to assess the potential real world injury reduction benefit of smart restraint systems in frontal impacts. The intelligent capability of the restraint was achieved by varying the seat belt load limiter (SBL) threshold, according to the frontal crash scenario. Real world accident data (CCIS) were analysed to identify the target population of vehicle occupants and frontal impact scenarios where employing smart load limiters could be most beneficial, particularly in reducing chest injury risk. From the accident sample, the chest was the most frequently injured body region at an AIS 2+ level in frontal impacts (7% of front seat occupants). The proportion of older vehicle front seat occupants (>64 years old) with AIS 2+ injury was also greater than the proportion of younger occupants. Additionally, older occupants were more likely to sustain seat belt induced serious chest injury in low and moderate speed frontal crashes. Numerical simulations using MADYMO software were conducted to examine the effect of varying the load limiter thresholds on occupant kinematics and injury outcome in frontal impacts. Generic baseline driver and front passenger numerical models were developed using a 50th percentile dummy and were adapted to accommodate a 5th and 95th percentile dummy. Simulations were performed where the load limiter threshold was varied in five frontal impact scenarios which were selected to cover as wide a range of real frontal crash conditions as possible. From the simulation results, it was found that for both the 50th and 95th percentile dummy in front seating positions (driver and passenger), the low SBL provided the best chest injury protection, without increasing the risk to other body regions. In severe impacts, the low SBL allowed the dummy to move further towards the front facia, thus increasing the chance of occupant hard contact with the vehicle interiors. The Smart load limiters predicted no injury risk reduction for the 5th percentile drivers, who are shorter and tend to sit closer to the steering wheel. The potential injury reduction of the smart load limiters was quantified by applying the estimated injury risk reduction from the simulation to the real world accident data sample. Thoracic injury predictions from the simulations were converted into injury probability values using AIS 2+ age dependent thoracic risk curves which were developed and validated based on a methodology proposed by Laituri et al. (2005). Real world benefit was quantified using the predicted relative AIS 2+ risk reduction and assuming an appropriate adaptive system was fitted to all the cars in the real world sample. When applying the AIS 2+ risk reduction findings to the weighted accident data sample, the risk of sustaining an AIS 2+ seat belt injury reduced from 1.3% to 0.9% for younger front seat occupants, 7.6% to 5.0% for middle aged front seat occupants and 13.1% to 8.6% for the older front seat occupants. The research findings clearly demonstrate a chest injury reduction benefit across all age groups when the load limiter characteristics are varied. It suggests that employing a smart load limiter in a vehicle would not only benefit older occupants but also middle aged and young occupants. The benefit does appear to be most pronounced for older occupants, since the older population is more vulnerable to chest injury. As the older population of car users is rapidly rising, the benefits of smarter systems can only increase in the future.
|Qualification||Doctor of Philosophy|
|Award date||1 Jan 2016|
|Publication status||Published - 2016|