Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  study a route summarization framework for Personalized Navigation dubbed PerNav - with which the goal is to generate more intuitive and customized turn-by-turn directions based on user generated content. The turn-by-turn directions provided in the existing navigation applications are exclusively derived from underlying road network topology information i.e., the connectivity of nodes to each other. Therefore, the turn-by-turn directions are simplified as metric translation of physical world (e.g. distance/time to turn) to spoken language. Such translation- that ignores human cognition about the geographic space- is often verbose and redundant for the drivers who have knowledge about the geographical areas. PerNav utilizes alth of user generated historical trajectory data to extract namely "landmarks" (e.g., point of interests or intersections) and frequently visited routes beten them from the road network. Then this extracted information is used to obtain cognitive turn-by-turn directions customized for each user.
['Y. Li', 'Han Su', 'Ugur Demiryurek', 'Bolong Zheng', 'Kai Zeng', 'Cyrus Shahabi']
I am writing a research paper on a idea PerNav: A Route Summarization Framework for Personalized Navigation
[{"from": "human", "value": "I am writing a research paper on a idea PerNav: A Route Summarization Framework for Personalized Navigation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  study a route summarization framework for Personalized Navigation dubbed PerNav - with which the goal is to generate more intuitive and customized turn-by-turn directions based on user generated content. The turn-by-turn directions provided in the existing navigation applications are exclusively derived from underlying road network topology information i.e., the connectivity of nodes to each other. Therefore, the turn-by-turn directions are simplified as metric translation of physical world (e.g. distance/time to turn) to spoken language. Such translation- that ignores human cognition about the geographic space- is often verbose and redundant for the drivers who have knowledge about the geographical areas. PerNav utilizes alth of user generated historical trajectory data to extract namely \"landmarks\" (e.g., point of interests or intersections) and frequently visited routes beten them from the road network. Then this extracted information is used to obtain cognitive turn-by-turn directions customized for each user."}]