SRTDrive: Safety-Aware Trajectory Refinement via Reference-Relative Supervision

End-to-end planners output a single final plan that can still collide with surrounding agents, even when a small adjustment could avoid the collision. Retraining the planner changes its weights and acquired behavior, and learned candidate scorers are not trained to judge a replacement relative to the original plan. We propose SRTDrive, a post-hoc refinement framework that leaves the planner frozen, keeps its output unchanged as a reference trajectory, and learns whether to retain or replace it. A generator produces alternatives as deformations of the reference along and across its path, so that every alternative is directly comparable to the original plan. A selector then scores each candidate relative to the reference and is trained so that a replacement that increases collision at any horizon is never the target, however close it is to the logged trajectory; trajectory error only ranks equally safe candidates. At inference, the top-ranked candidate, possibly the reference itself, is output without a collision threshold. On Bench2Drive, SRTDrive raises the Driving Score of VAD-Base from 43.07 to 47.77 and reduces the routes with collisions from 95 to 86, whereas the same candidates selected by a TTC rule do not yield these gains. On nuScenes, applied to seven frozen planners without retraining, it lowers L2 error on five and the collision rate on three, with the largest gains on VAD. The code will be released at https://srtdriveiclr2027.github.io/.