Authors: Lars Ullrich, Alex McMaster, Knut Graichen

Published on: April 12, 2024

Impact Score: 7.0

Arxiv code: Arxiv:2404.08271

Summary

• What is new: A study on transfer learning techniques using transformer-based models for trajectory planning in autonomous driving, focusing on adapting from simulation to real-world conditions.
• Why this is important: The challenge of adapting autonomous driving systems from simulations to real-world conditions, considering vehicle and country-specific differences such as sensor systems, traffic laws, and perception algorithms.
• What the research proposes: A simulation-based study investigating transfer learning techniques using a transformer-based architecture to address the adaptation needs of autonomous driving systems.
• Results: Insights into the trade-offs between computational time and performance, guiding effective model adaptations from simulation environments to real-world applications.

Technical Details

Technological frameworks used: Transformer-based architecture

Models used: Transfer learning techniques

Data used: Simulation data and real-world conditions adaptations

Potential Impact

Automotive industry, particularly companies focusing on autonomous driving technologies and their integration into real-world traffic systems.

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