  Current multi-modality driving frameworks normally fuse representation by
utilizing attention between single-modality branches. However, the existing
networks still suppress the driving performance as the Image and LiDAR branches
are independent and lack a unified observation representation. Thus, this paper
proposes MaskFuser, which tokenizes various modalities into a unified semantic
feature space and provides a joint representation for further behavior cloning
in driving contexts. Given the unified token representation, MaskFuser is the
first work to introduce cross-modality masked auto-encoder training. The masked
training enhances the fusion representation by reconstruction on masked tokens.
Architecturally, a hybrid-fusion network is proposed to combine advantages from
both early and late fusion: For the early fusion stage, modalities are fused by
performing monotonic-to-BEV translation attention between branches; Late fusion
is performed by tokenizing various modalities into a unified token space with
shared encoding on it. MaskFuser respectively reaches a driving score of 49.05
and route completion of 92.85% on the CARLA LongSet6 benchmark evaluation,
which improves the best of previous baselines by 1.74 and 3.21%. The introduced
masked fusion increases driving stability under damaged sensory inputs.
MaskFuser outperforms the best of previous baselines on driving score by 6.55
(27.8%), 1.53 (13.8%), 1.57 (30.9%), respectively given sensory masking ratios
25%, 50%, and 75%.
