Automated Driving Toolbox™ provides several features that support path planning and vehicle control.
To plan driving paths, you can use a vehicle costmap and the optimal rapidly exploring random tree (RRT*) motion-planning algorithm. You can also check the validity of the path, smooth the path, and generate a velocity profile along the path.
To design vehicle control systems, you can use lateral and longitudinal controllers that enable autonomous vehicles to follow a planned trajectory.
|Costmap representing planning space around vehicle|
|Store vehicle dimensions|
|Check vehicle costmap for collision-free poses or points|
|Check vehicle costmap for occupied poses or points|
|Get cost value of cells in vehicle costmap|
|Set cost value of cells in vehicle costmap|
|Collision-checking configuration for costmap based on inflation|
|3-D rigid geometric transformation|
|Create a quaternion array|
|Angular distance in radians|
|Quaternion frame rotation|
|Quaternion point rotation|
|Convert quaternion to rotation matrix|
|Convert quaternion to rotation vector (radians)|
|Convert quaternion to rotation vector (degrees)|
|Extract quaternion parts|
|Convert quaternion to Euler angles (radians)|
|Convert quaternion to Euler angles (degrees)|
|Convert quaternion array to N-by-4 matrix|
|Path Smoother Spline||Smooth vehicle path using cubic spline interpolation|
|Velocity Profiler||Generate velocity profile of vehicle path given kinematic constraints|
|Lateral Controller Stanley||Control steering angle of vehicle for path following by using Stanley method|
|Longitudinal Controller Stanley||Control longitudinal velocity of vehicle by using Stanley method|
Quaternions are four-part hypercomplex numbers that are used to describe three-dimensional rotations and orientations. Learn how to use them for automated driving applications.
Control the steering angle of a vehicle following a planned path and perform lane changing.