Intersections remain one of the most hazardous locations in urban road networks, where heterogeneous traffic participants and limited visibility frequently lead to severe traffic conflicts. In this paper, a vehicle-to-infrastructure-to-vehicle (V2I2V) cooperative system is proposed for improving road safety and traffic efficiency by using digital twins (DTs) deployed on roadside units (RSUs) to eliminate blind spots and centrally coordinate connected and automated vehicles (CAVs) in smart intersections. The proposed system integrates cloud-based global DTs for macroscopic guidance and RSU-based local DTs for real-time operations. Within this architecture, a hierarchical reinforcement learning (HRL) framework combines offline pre-training with online fine-tuning to achieve robust cooperative control. Experimental results show that the proposed system achieves substantial improvements in safety and efficiency in simulation experiments and real-world proof-of-concept (PoC) trials. In simulations, our system ensures high safety, efficiency, and smoothness under realistic communications and traffic constraints. In PoC trials, the RSU-centric control loop achieves a decision-making latency of approximately 42 ms and maintains a safe stopping distance of 8.5 m for pedestrians, while also shortening stop duration and overall traversal time. These results indicate that the proposed system provides robust and scalable performance at smart intersections.
Efficient and reliable path planning remains a core challenge for autonomous
vehicles operating in dynamic and crowded environments. Although Deep
Reinforcement Learning (DRL) has shown considerable potential in autonomous
decision-making, it still faces challenges such as insufficient feature
extraction, sparse rewards, and low obstacle avoidance efficiency in complex
scenarios. To address these issues, this paper proposes an end-to-end path
planning framework, PPO-ICM-Attn. Built upon the Proximal Policy Optimization
(PPO) algorithm, the framework incorporates a dual-channel attention
convolutional neural network module (Attention-CNN) to enhance spatial and
semantic understanding of dynamic obstacles, and introduces an Intrinsic
Curiosity Module (ICM) to promote active exploration in sparse-reward settings.
Furthermore, a reactive avoidance reward function based on velocity-obstacle
theory is designed and embedded to achieve real-time proactive collision
avoidance in highly dynamic environments. Experiments are conducted in a
semi-structured dynamic crowd scenario constructed on the GAZEBO simulation
platform. The results demonstrate that PPO-ICM-Attn achieves significant
improvements in key metrics such as path success rate, travel time, and path
efficiency compared to baseline methods like A*+DWA and standard DRL. Although
the gap remains in path efficiency compared to A*+DWA, the proposed method
exhibits superior robustness and navigation performance overall, validating its
effectiveness in complex dynamic environments.
Shiquan Shen, Jiahao Liu, Zheng Chen et al.· SAE technical paper series· 0 citations
