A motion model describes how a vehicle or robot moves from one state to the next. In autonomous systems, the motion model is essential because it gives the system a way to predict the future state before the next sensor update arrives.
Why motion models matter
- They support prediction in tracking and filtering
- They help estimate future pose and velocity
- They are used in localization, planning, and control
- They connect vehicle physics with sensor fusion
A simple example
For a vehicle moving in 2D space, the state may contain:
- x position
- y position
- heading angle
- velocity
If we know the current state and the time step, we can estimate where the vehicle should be next.
Common motion model assumptions
- Constant velocity
- Constant acceleration
- Constant turn rate and velocity
These models are simplified, but they are often useful enough for estimation and planning algorithms.
Where motion models appear
- Kalman Filters and EKF
- Particle filters
- Trajectory prediction
- Path planning and behavior planning
Final thoughts
A good motion model does not need to be perfect. It only needs to be accurate enough to support stable prediction between sensor updates and decisions.
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