Autonomous vehicle control
Autonomous vehicle stability control
With the recent development of autonomous vehicles, the level of autonomous vehicles has gradually increased. As the level increases, the degree of driver intervention in the autonomous driving state is decreasing. For this reason, vehicle stability in autonomous vehicles should be considered more important. Basically, for the stability of the vehicle in the autonomous driving state, the degree of steering angle, the driving force of the motor, and the pressure of the brake are determined. The controller is designed to follow the target value. During an emergency while driving, the driver intervenes in driving the vehicle. Depending on how quickly the driver's intervention is detected, vehicle's stability can also be secured.
Fig. Block diagram of autonomous vehicle stability control
Path planning of autonomous vehicle
< Collision Avoidance System >
Model predictive controller (MPC) is a popular optimization approach for tracking and planning path on an autonomous vehicle techniques. MPC is capable of generating control effort sequence within prediction horizon by predicting output of internal dynamics model as shown in Fig. 1.
Fig. 1. Description of model predictive control of autonomous vehicle
Fig. 2. Block diagram of autonomous vehicle control system using MPC
Collision avoidance is achieved by implementing artificial potential field (APF) in the optimization problem of MPC as described in Fig. 2. The MPC generates optimal path guaranteeing collision-free by minimizing objective function that is consist of tracking error and APF for collision avoidance. APF for obstacle is design based on relative velocity and distance between obstacle and ego vehicle. The total APF is sum of those of road boundary and obstacles as shown in Fig. 3. Fig. 4 shows the simulation result of collision avoidance system with popup obstacle.
Fig 3. Description of collision avoidance system with artificial potential field
Fig. 4. Autonomous vehicle path for popup obstacle avoidance simulation
Formation control have been extensively studied as expected for next generation technology of autonomous technology. Multiple unmanned agents such as drones, mobile robots, and ground vehicles are controlled in a centralized or distributed manner. Recently, many studies focus on dynamics based formation control to develop more applicable theory. Our research is about leader-follower formation control for tractor-trailer system considering vehicle dynamics. Especially, conventional leader-follower approach is combined with non-linear vehicle model controlled in a stability boundary generated by vehicle stability control technique.