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

Understand

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.
• This survey reviews representative Transformer-based autonomous driving models and organizes them by task role, sensing configuration, and architectural design.
• More importantly, it examines these models from a deployment-oriented perspective and analyzes how efficiency constraints reshape model design choices in practice.