This summary covers autonomous vehicles and data processing. For AVs, it outlines their history (from 1950s prototypes like the Firebird to Google Car/Waymo's development and industry cycles). Key technical components detailed include four core software modules: vehicle positioning, object detection, moving object prediction, and vehicle decision/control, alongside crucial technologies like ADAS and DMS. Automation levels (L0-L5), current market trends, development strategies, and real-world case studies (e.g., Taiwan's Xinyi Road autonomous bus) were presented. Significant potential issues were discussed, encompassing technical challenges (e.g., misjudgment, testing limitations), societal and legal dilemmas (e.g., insurance, the "Trolley Problem" ethics), and information security risks (e.g., data privacy, cyberattacks). The session also explored the data journey, explaining how raw data transforms into meaningful information through systematic data processing. Various data collection methods (e.g., search engines like Google Trends, social media, web scraping) were introduced, with a practical exercise using Python (version 3.10.5 recommended) and the Spyder IDE for web scraping and generating a word cloud for text analysis. Students are tasked with applying AI tools to create similar Python programs for data retrieval and visualization based on their chosen topics.

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