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Survey details Transformer models for autonomous driving

By PulseAugur Editorial · [1 sources] ·

This survey paper examines the use of Transformer-based models in autonomous driving systems. It categorizes these models by their task role, sensing configuration, and architecture, while also analyzing how efficiency constraints influence their design. The paper further reviews various compression and acceleration strategies, such as quantization and pruning, that are crucial for deploying these models in real vehicles. Ultimately, it highlights the need to consider compression as a fundamental system-level design element for safety and deployability. AI

IMPACT Provides a structured overview of current research and challenges in applying advanced AI models to autonomous vehicles.

RANK_REASON This is a survey paper on a specific AI application domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey details Transformer models for autonomous driving

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This is a survey paper on a specific AI application domain. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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137 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Juan Zhong, Yuhang Shi, Zukang Xu, Xi Chen ·

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

    arXiv:2304.10891v3 Announce Type: replace-cross Abstract: 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 p…

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