Probabilistic Free Space Mapping for Predicted Road Occupancy

The system performs trajectory predictions for multiple dynamic objects simultaneously, calculating probable future positions and confidence regions in advance. This preliminary action allows the driving function to plan ahead and execute gentle early reactions instead of waiting for certain collision risks, thereby improving safety while maintaining timely responses.

The free space map transitions from a static representation of current occupancy to a dynamic probabilistic model that continuously updates predicted trajectories and confidence regions. This dynamic approach allows the system to adapt to changing road user behaviors and anticipate future states, enabling earlier and more comfortable driving interventions.

Instead of uniformly processing all dynamic objects with the same level of detail, the system applies different prediction complexities based on local conditions. Objects closer to the ego vehicle or in critical positions receive more detailed trajectory predictions and confidence region calculations, while distant or less critical objects use simplified models. This local differentiation maintains comfort through adequate prediction where needed while reducing overall computational complexity.

The system dynamically adjusts prediction parameters such as prediction horizon, number of trajectory samples, and confidence region granularity based on the current driving situation. In calm traffic conditions, fewer predictions are performed with coarser resolution, while in complex or critical situations, the system increases prediction detail. This parameter adaptation enables comfortable driving when needed while managing computational resources efficiently.