Transformer-based models and hardware acceleration analysis in autonomous driving: A survey. (arXiv:2304.10891v1 [cs.LG])

Transformer architectures have exhibited promising performance in various
autonomous driving applications in recent years. On the other hand, its
dedicated hardware acceleration on portable computational platforms has become
the next critical step for practical deployment in real autonomous vehicles.
This survey paper provides a comprehensive overview, benchmark, and analysis of
Transformer-based models specifically tailored for autonomous driving tasks
such as lane detection, segmentation, tracking, planning, and decision-making.
We review different architectures for organizing Transformer inputs and
outputs, such as encoder-decoder …

analysis applications architectures arxiv autonomous autonomous driving autonomous vehicles become benchmark computational decision decoder deployment detection driving encoder encoder-decoder hardware making next overview paper performance planning platforms practical review segmentation survey tracking transformer