A hybrid planning method in autonomous vehicles is proposed to plan a best trajectory unction of a host vehicle. The hybrid planning method in autonomous vehicles ncludes a parameter obtaining step, a learning-based scenario deciding step, a earning-based parameter optimizing step and a rule-based trajectory planning step. The parameter obtaining step includes driving a sensing unit to sense a surrounding scenario of the host vehicle to obtain a parameter group to be learned and storing the parameter group to be learned to a memory. The learning-based scenario deciding step includes driving a processing unit to receive the parameter group to be learned from the memory and deciding one of a plurality of scenario categories that matches the surrounding scenario of the host vehicle according to the parameter group to be learned and a learning-based model. The learning-based parameter optimizing step includes driving the processing unit to execute the learning-based model with the parameter group to be learned to generate a key parameter group. The rule-based trajectory planning step includes driving the processing unit to execute a rule-based model with the one of the scenario categories and the key parameter group to plan the best trajectory function. Therefore, the present disclosure can not only process a plurality of multi-dimensional variables, but also be equipped with learning capabilities and conform to the continuity of trajectory planning.

Applied Country	Category	專利狀態	證書號	專利權期間	研究成果公告日期
TW	Intelligence and safety	獲證	I750762	2021/12/21~2040/08/05