Artificial Intelligence in Engineering Education in the Case of Self-driving Vehicle Curriculum

Rui Zhou,Peng Zhi,Xiaowei Xu,Zhilin Liu,Xiyun Li,Qingguo Zhou

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)（2022）

Lanzhou Univ | Xi An Jiao Tong Univ | Chinese Acad Sci CASIA

Cited 1|Views17

Abstract

Artificial Intelligence (AI) is currently a hot topic both in industry and engineering education. As a multidisciplinary field, self-driving vehicle (SDV) research has attracted much attention and is significant for applying and promoting AI. This paper presents our SDV curriculum for engineering education, which was developed with a fundamental basis in AI. The curriculum consists of five courses: Practical Methods Based on Robotics, Introduction to Artificial Intelligence, Innovation and Entrepreneurship, Digital Logic, and Embedded Development for Linux. Students with different academic backgrounds interested in AI or SDV can be trained by theoretical lectures and laboratory sessions at different levels. Students participating in the curriculum have inspired innovative ideas and practically implemented further work in the SDV field. In addition, we have produced educational resources including textbooks and an experimental SDV system. These case studies are discussed here. The curriculum has received highly positive feedback from students, which shows the effectiveness of our work. We are refining and promoting the curriculum for more students who are seeking knowledge and ability in AI and SDV fields.

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Key words

artificial intelligence,engineering education,multidisciplinary field,SDV curriculum,academic backgrounds,theoretical lectures,laboratory sessions,educational resources including textbooks,experimental SDV system,self-driving vehicle curriculum,robotics,innovation,entrepreneurship,digital logic,Linux embedded development

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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