  Simulating camera sensors is a crucial task in autonomous driving. Although
neural radiance fields are exceptional at synthesizing photorealistic views in
driving simulations, they still fail to generate extrapolated views. This paper
proposes to incorporate map priors into neural radiance fields to synthesize
out-of-trajectory driving views with semantic road consistency. The key insight
is that map information can be utilized as a prior to guiding the training of
the radiance fields with uncertainty. Specifically, we utilize the coarse
ground surface as uncertain information to supervise the density field and warp
depth with uncertainty from unknown camera poses to ensure multi-view
consistency. Experimental results demonstrate that our approach can produce
semantic consistency in deviated views for vehicle camera simulation. The
supplementary video can be viewed at https://youtu.be/jEQWr-Rfh3A.
