Open Access

ARTICLE

Planning by Simulation: A Query-Centric Search-Based Framework for Interactive Planning in Autonomous Driving

Tian Niu, Kaizhao Zhang, Zhongxue Gan, Wenchao Ding^*

College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China

* Corresponding Author: Wenchao Ding. Email:

(This article belongs to the Special Issue: Digital Twins and Virtual Engineering Systems for Sustainable and Intelligent Decision Making: Advanced Computational Modeling, Data Integration, and AI-Driven Simulation)

Computer Modeling in Engineering & Sciences 2026, 147(1), 32 https://doi.org/10.32604/cmes.2026.079324

Received 19 January 2026; Accepted 24 March 2026; Issue published 27 April 2026

Abstract

Ensuring operational safety for autonomous vehicles is a critical challenge in modern engineering, particularly due to the intricate interactions among diverse traffic participants. Traditional approaches often treat planning and prediction as unidirectional processes, failing to capture the dynamic, game-theoretic nature of real-world traffic. In the context of Digital Twins, there is an urgent need for high-fidelity virtual representations that can model the continuous, bidirectional evolution of the ego vehicle and surrounding agents to support robust decision-making under uncertainty. To address these limitations, a novel framework named Planning by Simulation with mutual influence prediction is proposed, which functions as a high-fidelity simulation-based predictive planner for autonomous driving decision-making. This framework explicitly models the iterative interplay between the ego vehicle’s planning and the predicted trajectories of surrounding agents within a virtual environment. By integrating a query-centric trajectory prediction mechanism with Monte Carlo Tree Search, the proposed approach orchestrates intelligent model exploration. It iteratively refines the ego vehicle’s actions by simulating future scenarios and adapting to the dynamic behaviors of other agents, thereby tightly coupling data-driven predictions with physics-based planning constraints. Comprehensive evaluations on the Argoverse 1 and Argoverse 2 dataset in Metadrive simulator demonstrate the efficacy of this simulation-based approach. The framework successfully captures complex interaction dynamics that static models overlook. The results indicate that the proposed method generates significantly safer, more rational, and human-like trajectories compared to existing baselines, validating the system’s high-fidelity predictive capabilities. The proposed framework illustrates the transformative potential of advanced virtual simulation technologies in autonomous mobility. By enabling the continuous integration of predictive data into the planning loop, this study provides a powerful foundation for interpretable and reliable decision-making in virtual engineering systems. It highlights how coupling generative simulation with interactive planning can resolve critical safety challenges in the lifecycle of intelligent autonomous systems.

---

Keywords

---