Macau Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China.
Comput Intell Neurosci. 2022 Jun 18;2022:4390394. doi: 10.1155/2022/4390394. eCollection 2022.
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.
本文提出了一种基于 Jetson nano 高性能嵌入式平台，利用机器视觉和深度学习技术实现的辅助驾驶系统。在多条件假设下建立车辆动力学模型，基于模型预测控制算法设计路径规划器和路径跟踪控制器，结合行为决策系统合理规划局部期望路径。基于有限状态机的行为决策算法根据环境变化合理转换驾驶状态，实现对目标车速的跟随，并能在有碰撞危险时及时采取有效紧急制动。系统可以通过模型预测控制算法完成运动规划，并控制自动驾驶车辆平稳跟踪重新规划的局部期望路径，完成变道超车动作，满足 ADAS 的需求。路径规划器基于 MPC 算法设计，求解具有避障功能的目标函数，规划可避免碰撞的最优路径，并使用 5 次多项式拟合输出的局部期望路径点。在 5∼8 s 的时间内，目标车辆减速至 48 km/h；自动驾驶车辆立即采取减速动作，并逐渐减小两车之间的速度差，直到达到目标速度，此时两车之间的距离接近安全距离，这是通过仿真测试结果得到的。当车辆在曲线上行驶时，系统仍能准确跟踪目标，并及时控制车辆期望速度的变化，使目标车辆始终保持安全距离。系统可在 50 米范围内使用。
