Ke Zhang

Short-horizon model predictive control runs fast but cannot see an opponent beyond its horizon, so it needs a learned, context-dependent cost correction; when passing is possible on either side, both corrections are good and deterministic regression averages them into the opponent. ZipDrift instead trains a one-step drifting generator over cost corrections, tilted by closed-loop cost and learned without differentiating through the optimiser, then generates a few candidates ranked by a learned critic. On a straight-track overtaking benchmark with an eight-dimensional stagewise correction, all 600 test episodes were collision-free, against a 4.0% crash rate for an idealised ZipMPC-style regressor.

Short-horizon model predictive control runs fast but cannot see an opponent beyond its horizon, so it needs a learned, context-dependent cost correction; when passing is possible on either side, both corrections are good and deterministic regression averages them into the opponent. ZipDrift instead trains a one-step drifting generator over cost corrections, tilted by closed-loop cost and learned without differentiating through the optimiser, then generates a few candidates ranked by a learned critic. On a straight-track overtaking benchmark with an eight-dimensional stagewise correction, all 600 test episodes were collision-free, against a 4.0% crash rate for an idealised ZipMPC-style regressor.