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    Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars

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    Event cameras are bio-inspired vision sensors that naturally capture the dynamics of a scene, filtering out redundant information. This paper presents a deep neural network approach that unlocks the potential of event cameras on a challenging motion-estimation task: prediction of a vehicle's steering angle. To make the best out of this sensor-algorithm combination, we adapt state-of-the-art convolutional architectures to the output of event sensors and extensively evaluate the performance of our approach on a publicly available large scale event-camera dataset (~1000 km). We present qualitative and quantitative explanations of why event cameras allow robust steering prediction even in cases where traditional cameras fail, e.g. challenging illumination conditions and fast motion. Finally, we demonstrate the advantages of leveraging transfer learning from traditional to event-based vision, and show that our approach outperforms state-of-the-art algorithms based on standard cameras.Comment: 9 pages, 8 figures, 6 tables. Video: https://youtu.be/_r_bsjkJTH

    Penerapan Kecerdasan Buatan dalam Industri MICE dan Event Di Indonesia: Tren, Potensi, dan Tantangan di Masa Mendatang

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    The MICE (Meeting, Incentive, Convention, and Exhibition) industry and events have undergone significant changes with the emergence of Artificial Intelligence (AI) technology. This research aims to explore the application and impact of Artificial Intelligence in the context of the MICE industry and events, particularly in Indonesia. Through an in-depth review of literature and case studies, this study discusses how AI technology has influenced the planning, execution, and participant experience in events. This research employs a qualitative descriptive method with an approach to literature study from various reliable sources, analysis of case studies of events that have adopted AI technology, as well as in-depth interviews with event organizers and industry experts. This information is collected and analyzed using a triangulation of data sources approach as a method to examine and validate data from various perspectives and gain deep insights into the application of AI in the MICE industry and events. The results of this study explain various ways in which AI can be integrated into MICE activities and special events. AI is used for data analysis, personalization of participant experiences, interaction through chatbots, and optimization of schedules and logistics. Furthermore, AI plays a role in event security and surveillance management. Facial recognition and video analysis solutions have been used to monitor visitors, identify suspicious behavior, and mitigate risks. AI platforms also monitor cybersecurity to ensure events proceed without disruptions. However, not all job roles can be replaced by AI; some roles related to hospitality and planning still require human intervention. The use of AI in the MICE and events industry also presents challenges, such as data security and privacy, as well as the proper utilization of technology. Nevertheless, the potential of AI in optimizing participant experiences, enhancing operational efficiency, and driving innovation continues to inspire event organizers to integrate this technology into their strategies. By understanding trends and the potential application of AI in the MICE and events industry, organizers and practitioners can design more engaging, efficient, and secure events in the evolving digital era, enriching the participant experience in future events
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