Abstract Autonomous vehicles (AVs), driven by state‐of‐the‐art deep learning and computer vision technologies, can revolutionize current mobility systems in modern transportation. Driverless AVs are slowly integrated into public transportation with significant advantages for the passengers and public transport operators. However, passenger safety and comfort are two of the main challenges that need to be addressed. This work presents a complete in‐cabin monitoring framework with a suite of services, employing deep learning algorithms using a variety of onboard sensors at the edge. This proposed framework offers various innovative services aimed at enhancing security, monitoring passenger presence, accommodating diverse needs, and personalizing the passengers' travel experience, while also reducing the workload of human safety officers. Experimental results demonstrate the framework's effectiveness in identifying abnormal events with a high accuracy, employing multiple datasets and custom in‐cabin scenarios. Additionally, the system effectively conducts automated passenger counting and facial identification, ensuring real‐time responsiveness under diverse operational conditions. Overall, the novelty of this work lies in the framework's multimodal approach, integrating visual and audio analysis, to achieve robust performance across various scenarios, significantly contributing to the advancement of autonomous driving technologies.
There is evidence that stars and browns dwarfs grow through episodic ratherthan continuous gas accretion. However, the role of episodic accretion in theformation of brown dwarfs remains mostly unexplored. We investigate the role ofepisodic accretion, triggered by the magnetorotational instability in the innerdisk regions, resulting in episodic outbursts during the formation of browndwarfs, and its implications for their early formation stages. We usehydrodynamical simulations coupled with a sub-grid accretion model toinvestigate the formation of young proto-brown dwarfs and protostars, takinginto account the effects of episodic accretion resulting in episodic radiativefeedback, i.e. in luminosity outbursts. The formation timescale for proto browndwarfs is at least one order of magnitude shorter than that of protostars.Episodic accretion leads to a shorter main accretion phase compared tocontinuous accretion in brown dwarfs, whereas the opposite is true for low-massstars. Episodic accretion can accelerate early mass accretion in proto-browndwarfs and protostars, but it results in less massive objects by the end of themain phase compared to continuous accretion. We find an approximately linearcorrelation between an object's mass at the end of the main accretion phase andthe timing of the last episodic outburst: later events result in more massivebrown dwarfs but less massive low-mass stars. Episodic outbursts have astronger effect on brown dwarf-forming cloud cores, with the last outburstessentially splitting the brown dwarf evolution into a short high-accretion anda much longer low-accretion phase.