Intelligent Driving Safety Monitoring Based on Machine Learning - An Example of Students Research Training Program in the Context of New Engineering Disciplines

The development of new engineering technologies such as artificial intelligence, machine learning, and big data analysis has prompted engineering colleges to keep up with the times in their courses and introduce relevant knowledge to cultivate professionals in different fields. Research training, as an important form of practical teaching in current colleges and universities, plays an important role in improving the innovation and entrepreneurship abilities of undergraduate students. This article takes a comprehensive research project conducted by the junior students at Beihang University as an example, and provides an example of cross disciplinary research training using emerging technologies such as machine learning and quality and reliability related professional knowledge in the context of new engineering. Quality and reliability are interdisciplinary fields involving aerospace, information, transportation, new energy, and other fields. Through scientific research training in new engineering, students can aim at these fields and comprehensively apply professional knowledge learned in the classroom and new engineering technologies mastered in practice to solve practical problems. This research training example focuses on comprehensive security monitoring in the field of intelligent transportation, using machine learning and other methods to process driver personnel data, environmental data, and vehicle data related to driving safety. Through training, mathematical models and methods that characterize the safety status of human-machine co driving are obtained, and corresponding software systems are developed. Although this training example is targeted at the emerging field of intelligent transportation, it also has certain reference significance for practical teaching in other professions and practical applications in other fields.