Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey

ArXi:2304.10891v2 Announce Type: replace-cross Transformer-based models are becoming a central paradigm in autonomous driving because they can capture long-range spatial dependencies, multi-agent interactions, and multimodal context across perception, prediction, and planning. At the same time, their deployment in real vehicles remains difficult because high-capacity attention-based architectures impose substantial latency, memory, and energy overhead.