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Automated driving has become a very promising research direction with many successful deployments and the potential to reduce car accidents caused by human error. Automated driving requires automated path planning and tracking with the ability to avoid collisions as its fundamental requirement. Thus, plenty of research has been performed to achieve safe and time efficient path planning and to develop reliable collision avoidance algorithms. This paper uses a data-driven approach to solve the abovementioned fundamental requirement. Consequently, the aim of this paper is to develop Deep Reinforcement Learning (DRL) training pipelines which train end-to-end automated driving agents by utilizing raw sensor data. The raw sensor data is obtained from the Carla autonomous vehicle simulation environment here. The proposed automated driving agent learns how to follow a pre-defined path with reasonable speed automatically.

Quarter-wave resonators are commonly used as acoustic silencers in automotive air induction systems. Similar closed side branches can also be formed in the idle air bypass, exhaust gas recirculation, and positive crankcase ventilation systems of engines. The presence of a mean flow across these side branches can lead to an interaction between the mean flow and the acoustic resonances of the side branch. At discrete flow conditions, this coupling between the flow and acoustic fields may produce high amplitude acoustic pressure pulsations. For the quarter-wave resonator, this interaction can turn the silencer into a noise generator, while for systems where a valve is located at the closed end of the side branch the large pressure pulsations can cause the valve to fail. This phenomenon is not limited to automotive applications, and also occurs in natural gas pipelines, aircraft, and numerous other internal and external flows.