  To address the challenges of sensor fusion and safety risk prediction,
contemporary closed-loop autonomous driving neural networks leveraging
imitation learning typically require a substantial volume of parameters and
computational resources to run neural networks. Given the constrained
computational capacities of onboard vehicular computers, we introduce a compact
yet potent solution named EfficientFuser. This approach employs EfficientViT
for visual information extraction and integrates feature maps via cross
attention. Subsequently, it utilizes a decoder-only transformer for the
amalgamation of multiple features. For prediction purposes, learnable vectors
are embedded as tokens to probe the association between the task and sensor
features through attention. Evaluated on the CARLA simulation platform,
EfficientFuser demonstrates remarkable efficiency, utilizing merely 37.6% of
the parameters and 8.7% of the computations compared to the state-of-the-art
lightweight method with only 0.4% lower driving score, and the safety score
neared that of the leading safety-enhanced method, showcasing its efficacy and
potential for practical deployment in autonomous driving systems.
