Data is an essential component of machine learning Data is becoming more influential every day. In fact, machine learning (ML) algorithms are becoming more powerful to learn and analyze data and add value to the business. Data is a new logic when interpreted correctly and efficiently, and multidimensional data can answer complex queries and solve problems with greater precision. The selection of valuable data, the extraction of features from statistical data, such as the mean and variance, and the observability of the data that lead to responsible Artificial Intelligence (AI), are part of data analysis. Additionally, data privacy and governance in cloud-based solutions and the ML development data collection process are critical and urgent requirements. Current landscape Existing ML or deep learning algorithms, such as object recognition and classification, will be further improved and developed in 2022. Many basic ML models with satisfactory performance are already available. More combinations of ML models or algorithms will become available to solve new and more complex features in the future. Currently, the focus is solely on implementing core functionality using AI. But safety, security, and many other non-functional quality requirements, such as uptime, are still not the focus of automotive engineers when using AI-based components. AI will have a long-term impact on any industry in the field of statistical analysis, rankings, predictions, or automation use cases. Below are the top 5 automotive areas where AI will influence in 2022. Manufacturing, humanoid robots and labor AI can replace repetitive jobs, and people who work primarily in manufacturing need to develop their skills. Robots are getting smarter to perform a limited number of tasks. There is currently a high level of intention to learn programming languages as a new skill, but due to new low-code/no-code platforms or the GPT3 Codex code generator under development, this doesn’t seem to be the right direction. Humanoid robots will benefit from advances in AI to perform routine human tasks that can be automated. Dangerous jobs are also areas where AI-controlled robots are the best solution. Maintenance, service and insurance Predictive maintenance is a new industry trend for AI that can also help the automotive industry predict breakdowns in vehicle parts and reduce the cost of the final product. Driver behavior could help the insurance company calculate the risk of individual drivers, improve the driving experience in cities, and ultimately reduce the number of accidents and increase road safety. Predictive maintenance uses digital twin technology to predict future system behavior. The concept of the digital twin, which combines the physical and virtual worlds, is not new, but it still helps in the validation of vehicle parts. Especially in the field of autonomous driving, the industry can benefit from digital twins. You can monitor the system using historical data and AI algorithms and predict future breakdowns to avoid system failures by exchanging associated parts or planning a maintenance activity in advance. Security, protection, validation and explainability Generative Adversarial Networks (GNAs) are a new approach to ML or data graphing that will dramatically improve safety and security analysis. Hazard Analysis and Risk Assessment (HARA) and Threat Analysis and Risk Assessment (TARA) in the security analysis process can be graphed to find out the relationships between nodes and edges using available statistical methods . Statistical techniques and corresponding ML algorithms can be used to further the analysis. RGAs can generate different valid driving scenarios that help identify unknown scenarios to test and validate complex vehicle functions. Identifying unknown unknowns is a major dilemma in all three areas of security and validation of new connected and automated vehicles. Explainable AI (XAI) informs why AI makes a decision as an integral part of self-driving car safety. It should be explained that an algorithm is unbiased and that the decision made is correct from the point of view of most people. An explanation is needed to ensure that the ML algorithm is mature enough to take control of the car and safely engage in traffic. Federated learning should be part of the ML pipeline to ensure privacy and data governance. Federated learning can answer the question of who owns the data. Environmental perception The number of vehicle sensors that record the vehicle environment is increasing due to autonomous driving technology and new individual ADAS (Advanced Driver Assistance Systems) functions. AI offers powerful tools for environmental perception such as convolutional neural networks for cameras or deep learning algorithms for radar sensors. Active learning improves AI performance and has an advantage over current passive supervised learning, which is used to train AI models. Active learning is the key to a mature AI model that uses real-world traffic scenarios to enable level 4 and 5 autonomous driving. Perspectives Ultimately, all AI activity should help society improve people’s quality of life to some extent. Whatever the industry, the responsible development of AI as an explainable AI must be above all advances in AI. As the amount of data collected increases, it must be clear where and why it is used. A global consensus is needed to respect human rights in general. Without these considerations, the next step in the development of Artificial General Intelligence (AGI) will complicate the situation. While human-level understanding is not yet possible, there will be a human version 2.0 as long as the AI can understand like a human, because the AI has access to all available knowledge. Until then, there is enough time to reach an agreement on the introduction and application of AI technology.]]>
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