EvoBelief: Evolving Behavioral Beliefs via Conformal Multimodal Diffusion

In driving, the ego car continually negotiates with other road users under imperfect perception, and the same observations often admit more than one reasonable maneuver—at a merge, a yield, or an unprotected turn the scene does not yet reveal whether to proceed or hold. Such interactive, ambiguous moments call not for a single confident trajectory, but for an explicit account of the competing behaviors the scene still admits. Diffusion-based trajectory planners can represent multiple plausible future behaviors, yet existing methods largely exploit this multimodality for trajectory generation rather than behavioral uncertainty estimation. We present EvoBelief, which treats the multimodal distribution of generated trajectories as explicit evidence of alternative driving behaviors, such as waiting or committing, and converts this evidence into safety-aware decisions. Since sample frequencies are not calibrated probabilities, EvoBelief uses split conformal prediction to calibrate behavioral evidence into statistically controlled safety decisions, with an online adaptive mechanism for deployment shift. On Bench2Drive, EvoBelief improves success rate from 89.25% to 91.36% over state-of-the-art. More broadly, EvoBelief offers a new paradigm that reads generative multimodality as an evolving behavioral belief and turns it into safe interactive driving decisions, rather than treating diffusion merely as a trajectory generator.