From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Driving

2026-03-18

AI Summary

Autonomous driving in the real world faces significant challenges, including data scarcity, stringent safety constraints, and limited generalization across diverse environments. This work presents a systematic review of synthetic data and virtual simulation techniques applied to perception, planning, and validation in autonomous systems. It proposes an integrated three-dimensional framework that combines synthetic data generation, digital twin–based validation, and domain adaptation, further enhanced by vision–language models to improve simulation fidelity and semantic generalization. By establishing a comprehensive taxonomy of current methodologies, the study identifies critical research directions—such as safety verification, cooperative autonomy, and simulation-driven policy learning—to advance the development of scalable, safe, and generalizable autonomous driving systems.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Vision for Robotics & Autonomous Driving
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Autonomous driving technologies have achieved significant advances in recent years, yet their real-world deployment remains constrained by data scarcity, safety requirements, and the need for generalization across diverse environments. In response, synthetic data and virtual environments have emerged as powerful enablers, offering scalable, controllable, and richly annotated scenarios for training and evaluation. This survey presents a comprehensive review of recent developments at the intersection of autonomous driving, simulation technologies, and synthetic datasets. We organize the landscape across three core dimensions: (i) the use of synthetic data for perception and planning, (ii) digital twin-based simulation for system validation, and (iii) domain adaptation strategies bridging synthetic and real-world data. We also highlight the role of vision-language models and simulation realism in enhancing scene understanding and generalization. A detailed taxonomy of datasets, tools, and simulation platforms is provided, alongside an analysis of trends in benchmark design. Finally, we discuss critical challenges and open research directions, including Sim2Real transfer, scalable safety validation, cooperative autonomy, and simulation-driven policy learning, that must be addressed to accelerate the path toward safe, generalizable, and globally deployable autonomous driving systems.

Problem

• autonomous driving
• data scarcity
• safety validation
• generalization
• Sim2Real transfer

Innovation

• synthetic data
• digital twin
• domain adaptation
• vision-language models
• Sim2Real transfer