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Implement probabilistic collision avoidance based on Wolinski, Lin, and Pettré (2016). The model uses warped intrinsic fields to predict collisions and gradient descent to compute avoidance velocities. Full three-layer integration: C++ core, pybind11 bindings, Python wrapper. Includes system tests, bidirectional benchmark, and documentation.
- Remove agent-agent repulsion forces from WarpDriverModel (speed model, not force model) - Add small random lateral perturbation on trajectory samples to break symmetry in head-on encounters - Rename boundary repulsion to boundary steering for consistency - Move WarpDriver docs into pedestrian_models/, add short description in index.rst with link to full model page - Document agent steering as inactive option for future use
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Although the mode calculates fewer steps, it's overall a bit slower most other models. In this PR the model will be introduced. Optimised, if needed, in another step. 
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Preview: https://PedestrianDynamics.github.io/jupedsim/pull-requests/1545/