The invention discloses a brain-controlled driving method and system based on deep learning environment perception. The method comprises the following steps: constructing a brain-computer interface control normal form of sparse gradient evoked potential; obtaining target electroencephalogram signal data based on the brain-computer interface control normal form; processing the target electroencephalogram signal data to obtain a user control intention, and mapping the user control intention to a linear velocity-angular velocity space of the driving equipment; acquiring surrounding environment information of the driving equipment based on a deep learning network; and based on a dynamic environment adaptive control algorithm, integrating the user control intention and the surrounding environment information of the driving equipment, and performing man-machine cooperative control decision. According to the method, the problems of low brain control efficiency and difficulty in adaptability in a complex dynamic environment caused by non-coupling control of the linear speed and the angular speed of an existing traditional brain control driving system can be solved.
本发明公开了一种基于深度学习环境感知的脑控驾驶方法及系统,方法包括:构建稀疏梯度诱发电位的脑机接口控制范式;基于所述脑机接口控制范式,获取目标脑电信号数据;对目标脑电信号数据进行处理,获取用户控制意图,并将所述用户控制意图映射到驾驶设备的线速度‑角速度空间;基于深度学习网络获取驾驶设备周围环境信息;基于动态环境自适应控制算法,整合所述用户控制意图与所述驾驶设备周围环境信息,进行人机协同控制决策。本发明能够解决现有传统脑控驾驶系统因线速度与角速度的非耦合性控制导致的脑控效率低下,以及难以在复杂动态环境中的适应性问题。
Brain-controlled driving method and system based on deep learning environment perception
一种基于深度学习环境感知的脑控驾驶方法及系统
2025-04-08
Patent
Electronic Resource
Chinese
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