In traditional control systems, people rely on employing devices such as handles and joysticks to achieve human-machine interaction with external devices, which is a challenge for patients with movement disorders. Meanwhile, brain-computer interface (BCI) technology can convert EEG into control commands for external devices through the brain loop, allowing these patients to directly control external devices by their brain’s “consciousness”. This study proposes an autonomous driving system of intelligent car based on multimodal BCI to integrate the subjects’ EEG, electro-oculography, and gyroscope signals to control the car. EEG is used for controlling the car speed, electrooculography for controlling the start and stop of the car, and gyroscope signals for controlling the car steering. Additionally, computer vision technology is combined to add autonomous driving function for the intelligent car, making control more intelligent. The experiments show that the average accuracy rate of ten subjects utilizing the system to control the car is 92.47%, with an average response time of 1.55 s and an average information transmission rate of 55.94 bit/min, which indicates the effectiveness and efficiency of the control system. Meanwhile, multiple comparative experiments for verification are set up to verify the car’s autonomous driving function. The experimental results show that compared with manual driving, although the autonomous driving system has disadvantages in controlling the car speed, it has better performance advantages in accuracy and stability. This proves that this system can provide better control experience for the disabled, and has broad application prospects in brain control and autonomous driving.