Artificial intelligence (AI) will change society in so many different aspects that it is not comprehensible to grasp yet. It will be on the same magnitude as the Industrial Revolution (or most likely much larger). AI carries threats and great opportunities. Given the unknown unknowns of AI, we can spot headlines every day with either alarming news or the opposite. AI plays a major role for enabling autonomous vehicles but also for the development of more sophisticated and efficient advanced driver assistance systems (ADASs) by supporting, for example, rapid object detection and classification. Since the mid-20th century, AI has been a research topic, but not until recently has it gained momentum due to, for example, the performance increase in ubiquitous hardware. It is computationally expensive to execute AI algorithms. There are broadly speaking three “types” of AI discussed today, machine learning (ML), deep learning, and generative AI (GenAI), where GenAI is a specialized case of deep learning, which in turn is a subset of ML. The common denominator for all AI is training data, and data fit for purpose are pivotal for the development of AI algorithms. Garbage in will result in garbage out.

中文翻译：

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人工智能和车辆[联网和自动驾驶车辆]

人工智能（AI）将从许多不同的方面改变社会，以至于目前还无法理解。其规模将与工业革命相同（或者很可能更大）。人工智能既带来威胁，也带来巨大机遇。考虑到人工智能的未知数，我们每天都能看到头条新闻，要么是令人震惊的消息，要么是相反的消息。人工智能不仅在实现自动驾驶汽车方面发挥着重要作用，而且还通过支持快速物体检测和分类等功能，在开发更复杂、更高效的高级驾驶员辅助系统 (ADAS) 方面发挥着重要作用。自 20 世纪中叶以来，人工智能一直是一个研究主题，但直到最近，由于无处不在的硬件性能的提高，人工智能才获得了发展势头。执行人工智能算法的计算成本很高。今天讨论的人工智能大致可分为三种“类型”：机器学习 (ML)、深度学习和生成式人工智能 (GenAI)，其中 GenAI 是深度学习的一个特例，而深度学习又是 ML 的子集。所有人工智能的共同点是训练数据，而适合用途的数据对于人工智能算法的开发至关重要。垃圾进来将导致垃圾出去。