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Current state-of-the-art vehicles implement pedestrian protection features that rely on pedestrian detection sensors and algorithms to trigger when impacting a pedestrian. During the development phase, the vehicle must “learn” to discriminate pedestrians from the rest of potential impacting objects. Part of the training data used in this process is often obtained in physical tests utilizing legform impactors whose external biofidelity is still to be evaluated. This study uses THUMS as a reference to assess the external biofidelity of the most commonly used impactors (Flex-PLI, PDI-1 and PDI-2). This biofidelity assessment was performed by finite element simulation measuring the bumper beam forces exerted by each surrogate on a sedan and a SUV. The bumper beam was divided in 50 mm sections to capture the force distribution in both vehicles. This study, unlike most of the pedestrian-related literature, examines different impact locations and velocities.

We propose to enhance reliability based diagnosis by enhancing the fault tree model with a sensor layer for capturing evidence. We recognized the need for an automated diagnostic process that can predict and report component failure in vehicles prior to total failure of any system in the vehicle. We also want to take advantage of evidence that can be derived from sensors to reduce the amount of tests required to identify failed components.

The University of Virginia is investigating the use of a magnetohydrodynamic (MHD) angular rate sensor to measure head angular acceleration in impact testing. Output from the sensor, which measures angular velocity, must be differentiated to produce angular acceleration. As a precursor to their use in actual testing, a torsional pendulum was developed to analyze an MHD sensor's effectiveness in operating under impact conditions. Differentiated and digitally filtered sensor data provided a good match with the vibratory response of the pendulum for various magnitudes of angular acceleration. Subsequent head drop tests verified that MHD sensors are suitable for measuring head angular acceleration in impact testing.

Infrastructure systems such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2X) communication can theoretically prevent nearly all accidents by gathering the speed, locations, and travel directions of traffic participants, and intervening to control vehicle motion as required to help prevent collisions. However, during the phase-in of the communication systems, there will be many vehicles and many roads that do not have the communication systems in place, and therefore the system will not be effective in those cases. This lack of availability is likely the main disadvantage. On-board sensing (autonomous) systems such as cameras and radar sensors may not detect all potential hazards (e.g. due to weather, or hidden hazards), but they are effective in many situations and can help prevent crashes without depending on communication with infrastructure or other vehicles.

The aim of the study was to investigate the difference between car-to-e-bikes and car-to-pedestrian accidents. The China In-depth Accident Study (CIDAS) database was searched from 2011 to 2013 for pedestrians and e-bikes struck by car, van and SUV fronts, which resulted in 104 pedestrian and 85 e-bike cases where information was sufficient for in-depth analysis. Reconstruction by PC-Crash was performed for all of the sampled cases. Pre-crash parameters were calculated by a MATLAB code. Focus was on prototypical accident scenarios and causes; speed as well as possible prevention countermeasures. It has been shown that traffic light violations, road priority violations, and unsure safety (these situations included misjudgments, unpreparedness, proximity to other road users, inappropriate speeds, etc.…) are the main causes in both the VRU groups. Distinctions were found for aspects of car collision speed, accident scenario, distribution of head contact points and so on.

As accurate measuring of head accelerations is an important aspect in predicting head injury, it is important that the measuring sensor be well-coupled to the head. Various sensors and sensor mounting schemes have been attempted in the past with varying results. This study uses a small, implantable acceleration sensor pack in the ear to study impact coupling with the human skull. The output from these ear-mounted accelerometers is compared to laboratory reference accelerometers rigidly attached to the skull of two cadaveric head specimens for both low-amplitude oscillatory tests and high-amplitude impact drop tests. The combination of sensor type and mounting scheme demonstrates the feasibility of using ear mounted sensors to predict head acceleration response. Previously reported progressive phase lag was not seen in this study, with the comparison between ear mounted accelerometers and rigidly mounted head accelerometers ranging from very good to excellent.