ArticleResearch (Washington, D.C.)2024
Parallel Driving with Big Models and Foundation Intelligence in Cyber-Physical-Social Spaces.
Article in Research (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Embodied cognition-driven interpretable trajectory prediction of autonomous systems.Nature communications · 2026Article
- Multi-modal and multi-agent reinforcement learning framework for urban traffic flow prediction and signal control optimization.Scientific reports · 2026Article
- From Controlled Scenarios to the Real World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration.Research (Washington, D.C.) · 2026Article
- Multi-horizon flight trajectory prediction enabled by time-frequency wavelet transform.Nature communications · 2025Article
- Analyzing Mega-mobility Systems in Smart Cities: A Macro-Micro Integration with Feedback Paradigm Empowered by Artificial Intelligence.Research (Washington, D.C.) · 2025Review
- Orchestrating Embodied Systems through the Embodied Context Protocol: Motivation, Progress, and Directions.Research (Washington, D.C.) · 2025Review
- RoadDiffBox: Automatic Road Distress Diagnosis through Controlled Image Generation and Semi-Supervised Learning.Research (Washington, D.C.) · 2025Article
- ViE-Take: A Vision-Driven Multi-Modal Dataset for Exploring the Emotional Landscape in Takeover Safety of Autonomous Driving.Research (Washington, D.C.) · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs); on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the "6S" goals of parallel driving.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.