You can use model predictive control (MPC) in automated driving applications to improve vehicle responsiveness while maintaining passenger comfort and safety. MPC has several features that are useful for automated driving, such as predicting vehicle behavior in the near future and explicitly handling constraints during optimization. For more information, see Automated Driving Using Model Predictive Control.
|Adaptive Cruise Control System||Simulate adaptive cruise control using model predictive controller|
|Lane Keeping Assist System||Simulate lane-keeping assistance using adaptive model predictive controller|
|Path Following Control System||Simulate path-following control using adaptive model predictive controller|
You can design and simulate automated driving systems using MPC controllers.
Design an MPC controller that tracks a set velocity and maintains a safe distance from a lead vehicle by adjusting the longitudinal acceleration of an ego vehicle.
Design an adaptive cruise control system that detects a lead vehicle in its environment by combining data from vision and radar sensors.
Design an MPC controller that keeps an ego vehicle traveling along the center of a straight or curved road by adjusting the front steering angle.
Design an MPC-based lane-keeping assist system that uses lane detection and road curvature previewing from the Automated Driving Toolbox™.
Design an MPC-based lane-following system that uses lane detection and road curvature previewing from the Automated Driving Toolbox.
Design an MPC-based lane-following system that detects lane and vehicles using a camera system simulated using the Unreal Engine®.
Design a lane-following controller using nonlinear MPC with road curvature previewing.
Design a lane-change controller using a nonlinear MPC controller.
Use adaptive MPC to make a vehicle follow a reference velocity and avoid obstacles by updating the plant model and linear mixed input/output constraints at run time.