UC Berkeley

Modern automobiles are equipped with various driver assistance functions which enhance safety and relieve driver fatigue. With the recent development of sensor technology, the Adaptive Cruise Control (ACC) system has been put into practice. This thesis investigates several aspects for the ACC system including (1) smooth reaction of the host vehicle to the cutting in and out of lead vehicles, (2) real-time optimal profile generation for stop-and-go motions, (3) optimal feedback controller design, and (4) extension to Cooperative Adaptive Cruise Control (CACC) systems.

The ACC system should maintain an appropriate relative distance to the lead vehicle and should also maintain the desired speed set by the driver if there is no lead vehicle or if the speed of the lead vehicle is faster than the desired speed. Also, it should react smoothly when the lead vehicle cuts out or if a new lead vehicle cuts in from a side lane. This thesis introduces the virtual lead vehicle scheme to prevent the switching between the distance control and the speed control. By controlling the motion of the virtual lead vehicle to be smooth, the scheme could provide smooth reaction of the host vehicle to the cutting in and out of lead vehicles. Linear Quadratic (LQ) optimal control scheme is utilized to find the control gains for the virtual lead vehicle and the host vehicle. Variable weights are utilized in LQ for the virtual lead vehicle. With the variable weights, the motion of the virtual lead vehicle is controlled to be smooth when there is no safety threat while ensuring that the virtual lead vehicel is still responsive and fast when a dangerous situation occurs. ACC with Stop-and-Go and the Cooperative Adaptive Cruise Control (CACC) system are extensions of the conventional ACC system. Stop-and-Go system is targeted to be used in urban driving situation where the lead vehicle can stop completely. In that case, the Stop-and-Go system should have a capability to stop the host vehicle completely. The constant time-headway policy used to find the appropriate relative distance causes undesirable motion for a complete stop. In this thesis a sliding controller is utilized to control the complete stopping motion. To find the optimal stopping trajectory, a constrained Quadratic Programming (QP) problem is solved. A constrained QP is also used to find the optimal velocity profile when the stopped vehicle is to resume motion. Multi-resolution formulations and the Lemke algorithm are utilized to find the optimal trajectories in real time. The CACC system utilizes wireless communication so that the vehicles in the network can share information with other vehicles. In this thesis, a centralized controller is designed by LQ optimal control scheme and potential benefits and problems are addressed. A Kalman filter with variable measurement noise covariance is introduced to compensate the lost data through the wireless network associated with the CACC system. The proposed control schemes have been verified through simulations.