CALM

Comfort-Aware Longitudinal Motion

Slow down for the bump, not for the sake of it.

Every driver knows the moment. A speed bump comes up, you have half a second to decide, and the car answers with a jolt that should have been a ripple. CALM is an open research project about that decision: how a car can read the road ahead and ease off by exactly as much as the bump deserves, so the people inside barely notice it.

CALM began as a bachelor thesis at TU Berlin and has grown into an open source stack for comfort-aware speed recommendation. It contributes:

• An open source software stack for the slowdown-depth decision: a half-car simulator with nonlinear suspension, a Gymnasium environment, an acados MPC, a PPO policy and ISO 2631-1 comfort scoring.
• The CALM dataset: 159 real bump crossings from CARD (114 Germany, 45 Italy; 105 usable), an extended real-time dataset for comfort.

The crossings

Height and width of the bumps in the recorded crossings, one color per area. The dotted and dashed lines mark equal severity, the height divided by the width.

Germany

Italy

Models and data

Code

The simulator and the planners are on GitHub, together with the MATLAB ride models in ride-models-matlab.

Acknowledgements: the real-road recordings build on the CARD dataset (Elazab et al., CVPR 2026).