Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment

In this paper, we propose an assisted driving system implemented with a Jetson nano-high-performance embedded platform by using machine vision and deep learning technologies. The vehicle dynamics model is established under multiconditional assumptions, the path planner and path tracking controller are designed based on the model predictive control algorithm, and the local desired path is reasonably planned in combination with the behavioral decision system. The behavioral decision algorithm based on finite state machine reasonably transforms the driving state according to the environmental changes, realizes the following of the target vehicle speed, and can take effective emergency braking in time when there is a collision danger. The system can complete the motion planning by the model predictive control algorithm and control the autonomous vehicle to smoothly track the replanned local desired path to complete the lane change overtaking action, which can meet the demand of ADAS. The path planner is designed based on the MPC algorithm, solving the objective function with obstacle avoidance function, planning the optimal path that can avoid a collision, and using 5th order polynomial to fit the output local desired path points. In 5∼8 s time, the target vehicle decelerates to 48 km/h; the autonomous vehicle immediately makes a deceleration action and gradually reduces the speed difference between the two vehicles until it reaches the target speed, at which time the distance between the two vehicles is close to the safe distance, obtained by the simulation test results. The system can still accurately track the target when the vehicle is driving on a curve and timely control the desired speed change of the vehicle, and the target vehicle always maintains a safe distance. The system can be used within 50 meters.