Diffusion-based driving planners capture diverse behaviors but can generate unsafe trajectories under distribution shift. We propose BridgeGuard, a safety-constrained diffusion planning method that progressively strengthens a constraint term during denoising to drive intermediate trajectories toward a scene-dependent safety domain. Corrections operate in a low-dimensional curve space, promoting geometric coherence. A learned module, DistanceFieldNet, predicts a time-dependent distance field from bird's-eye-view features. Value and spatial-gradient supervision at queries sampled beyond expert trajectories teaches this field about both safe and unsafe regions. The learned field supplies the constraint term through safety injection while the pretrained perception backbone and planner remain frozen. We further establish sufficient conditions for terminal safety in an idealized continuous-time bridge. On Bench2Drive, BridgeGuard improves driving score/success rate from 87.99/74.99% to 90.88/76.36% for BridgeDrive and from 80.79/58.18% to 90.46/74.09% for DiffusionDrivegeo, demonstrating cross-model generalization.
Figures & tables
Figure 1: BridgeGuard safety correction. A learned distance field guides curve refinement during denoising, promoting geometric coherence. Terminal safety is guaranteed only for the idealized continuous-time bridge under the stated assumptions.
Figure 2: DistanceFieldNet training queries. Expert-only sampling (left) covers a narrow, typically safe region. Anchor-based sampling (right) broadens coverage to safe (green) and violating (red) queries beyond the expert path.
Figure 3: BridgeGuard inference. Green components denote BridgeGuard additions. At each denoising step, DistanceFieldNet supplies gradients for curve correction; resampled waypoints are returned to the frozen planner’s solver.
Table 1: Comparison of different methods on Bench2Drive.
Table 5
Figure 4: Unprotected left turn on Bench2Drive. BridgeDrive (blue) collides with an oncoming vehicle; BridgeGuard (orange) avoids the collision by bypassing the vehicle. See also Appendix H .
Figure 5: Multi-lane change on Bench2Drive. BridgeDrive (blue) enters an occupied lane; BridgeGuard (orange) yields to the white vehicle and merges safely. See also Appendix H .
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Component
Configuration
Inputs
Shared BEV features, waypoint coordinates, and corresponding time horizons
BEV feature shape
(B,64,64,64)
Convolutional stem
FBEV∈RB×64×H×W→F0∈RB×32×H×W
Spatial query encoding
Sinusoidal positional encoding and MLP; dimensions = 256
Temporal query encoding
Sinusoidal positional encoding and MLP; dimensions = 256
Query normalization
Position scaled by BEV metric extent ( ymax=xmax=32 )