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In this paper the application of Statistical Energy Analysis (SEA) to the sound package design for a fuel cell powered sedan is presented. Fuel cell vehicles represent a different challenge to a vehicle with a conventional powertrain. With the replacement of the internal combustion engine (ICE), a principal source of airborne and structure-borne powertrain noise, the expectation is that the cabin noise levels would be significantly reduced as the main noise sources would be road and wind noise. A fuel cell powertrain, however, has a number of mechanical sources on the body structure that will radiate airborne noise and may transmit significant structure-borne noise to the vehicle interior. With this alternative power train, much of the conventional wisdom on vehicle sound package developed from experience with ICE's must be reconsidered.

Exhaust valve guttering is a corrosive process that takes place on the valve seat face. Detroit Diesel Allison Division of General Motors and Eaton Corporation have developed an accelerated test to gutter exhaust valves. The test was used to rank seven factors considered to be significant oil consumption, lubrication oil ash content and injector timing were found to be the major contributors to guttering.

This paper presents the computer vision-based V2X collaborative perception. Our system uses a forward-looking camera in the host vehicle. The camera detects road users such as pedestrians, vehicles, and motorcycles. Such information includes object type, relative location, direction, and speed. This information is used to compose proxy Basic Safety Messages on behalf of the detected objects. Early adopters of the V2X technology can experience the benefits of enhanced V2X market penetration. The outcome of adopting this concept will result in an inflated V2X market penetration rate leading to earlier safety, mobility, and situational awareness improvements. The ultimate goal is for all road participants to be fully aware of each other. The novelty of our work is the integration of computer vision-based detection and LTE-V V2X communications, in addition to implementing the concept for pedestrians and bicyclists.

Vehicle to Everything (V2X) technology has been studied extensively in the past years. Limited pilot and production deployments, and research work have demonstrated V2X benefits. These include improvement to safety, mobility conditions and environmental benefits. Several safety and mobility applications have been investigated in literature. Nonetheless, V2X holds a potential for broader innovation in connected and automated vehicle applications. Feasibility assessment and algorithm validation of such applications may prove to be challenging. This results from costs associated with test track rental and equipping vehicles with V2X technology. Besides, limited V2X penetration rate leads to unavailability of naturalistic testing environment. In this paper, we investigate the use of the autonomous vehicle simulation, named Carla, for V2X application validation. Carla is an open source project that we altered to enable V2X applications assessment.

At certain key stages in the vehicle development process, prototype vehicles are available for NVH testing. This testing fulfills two functions: primarily it is used to assess the status of the vehicle to the program NVH performance targets, but it also provides an opportunity to validate the vehicle SEA model. These single vehicle test events provide a snapshot of the NVH performance but do not provide any understanding of the variability of the NVH performance, which is due to many factors: components, build or assembly and test setup variability. SEA models can be used to estimate the vehicle level variability, if the variability of the interior components is understood, but there is limited data available to confirm the accuracy of these predictions. In this paper we examine the repeatability and reproducibility through a standard gage R&R study of Engine Noise Reduction (engine NR) and Tire NR testing.