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Parallel Driving with Big Models and Foundation Intelligence in Cyber-Physical-Social Spaces
Xiao Wang1, Jun Huang2, Yonglin Tian3
1School of Artificial Intelligence, Anhui University, Hefei, China.
Research (Washington, D.C.)
|May 21, 2024
Summary
Foundation intelligence, integrating metaverse and digital twins, creates parallel driving training spaces for connected and autonomous vehicles (CAVs), enhancing ITS safety and efficiency.
Area of Science:
- Intelligent Transportation Systems (ITSs)
- Artificial Intelligence (AI)
- Cyber-Physical-Social Systems
Background:
- Connected and autonomous vehicles (CAVs) introduce complexity to ITSs.
- Digital twins (DTs) offer precise, real-time ITS modeling.
- Metaverse technologies enhance ITSs with AI and DTs for sustainability, efficiency, and safety.
Purpose of the Study:
- To explore foundation intelligence for parallel driving in cyber-physical-social spaces.
- To elucidate the characteristics and mechanisms of big models for CAV training.
- To discuss future trends and research directions for parallel driving.
Main Methods:
- Integration of metaverse and digital twins (DTs) to create a parallel training space.
- Application of big models and foundation intelligence for CAVs.
- Cyber-physical-social space modeling for ITS.
Main Results:
- Development of a parallel training space for CAVs using metaverse and DTs.
- Embodiment of foundation intelligence for advanced AI applications in ITS.
- Comprehensive elucidation of parallel driving mechanisms and characteristics.
Conclusions:
- Foundation intelligence is crucial for advancing parallel driving in ITS.
- The integration of metaverse and DTs provides a robust framework for CAV training.
- Future research should focus on the "6S" goals of parallel driving and emerging trends.
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