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Autonomous Vehicles (AVs) operate based on image information and 3D maps generated by sensors like cameras, LIDARs and RADARs. This information is processed by the on-board processing units to provide the right actuation signals to drive the vehicle. For safe operation, these sensors should provide continuous high quality data to the processing units without interruption in all driving conditions like dust, rain, snow and any other adverse driving conditions. Any contamination on the sensor surface/lens due to rain droplets, snow, and other debris would result in adverse impact to the quality of data provided for sensor fusion and this could result in error states for autonomous driving. In particular, snow is a common contamination condition during driving that might block a sensor surface or camera lens. Predicting and preventing snow accumulation over the sensor surface of an AV is important to overcome this challenge.

Whiplash-associated disorder is one of the most common injuries from rear-impact crash scenarios. Knowing the injury mechanism is one of the keys in designing the seat to reduce the risk of injury. Due to the effects of variation, whiplash prevention is one of the most challenging safety-related topics in automotive industry. The test variation can originate from the dummy itself, seat components, materials, assembly tolerance, and as well as typical test setup variations. It is important to understand these variations and take them into account using Computer-Aided Engineering (CAE) analysis in order to identify how to reduce the risk of injury. In this paper, a robust methodology to predict occupant response from CAE simulations is developed by combining a Design of Experiment (DOE) with an Automated Process (AP). A Whiplash Variation Map (WVM) is developed to serve as a seat design aid.

The objective of this work was to create mathematical models of the BioSID side impact test dummy, for use in side impact simulation studies. Two dummy models were created - a multibody model, and a finite element model. The models have been validated according to the procedures described in the BioSID User's Manual[1]. The responses of both models were within the required corridors for most of the specified calibration tests, and during these test conditions the behaviour of the models correlated well with physical dummies. The models are now being tested in numerical side impact models of the vehicle. Each dummy model is particularly suited to different tasks: The multibody model is used in a lumped-mass model of the vehicle side structure, for studying the door padding and door intrusion characteristics.