I am planning to use textual rules like traffic rules in motion planning of autonomous car. I can think of using BERT like models to generate embeddings and then use these embeddings for motion or trajectory planning. My question is should I go for things like ontology, knowledge graph instead of embeddings of Natural language processing (NLP) model? Are embeddings of NLP models usable for motion or trajectory planning, or they need some other extra layer after embedding generation?

Note: I have worked with computer vision, where I have used embeddings or feature vector in lot of problem domain, but new in the domain of NLP and knowledge representation.

  • $\begingroup$ I haven't worked with anything like this, but I think you can use OpenAIs DALL·E as inspiration. You task should in principle be easier (DALL·E generates images from text). arxiv.org/abs/2102.12092 $\endgroup$
    – Avatrin
    Nov 28 at 23:13

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