Smart Medical Rescue via Efficient Vehicle Road Cooperation: AIoT Framework

Smart medical rescue vehicles (SMRVs) are crucial in providing timely and effective emergency medical services in urban environments. However, the efficiency and safety of SMRVs are often hindered by dynamic and complex traffic conditions, leading to longer response times and increased risk of accidents. To address these challenges, this article proposes a novel lane-changing strategy called AIoT-LC, which leverages the Augmented Intelligence of Things (AIoT) framework to enable efficient vehicle road cooperation for smart medical rescue. First, a deep Q-network is employed to process real-time data streaming from SMRVs and support their collaborative management with roads and pedestrians. Then, a cooperative lane-changing strategy is developed to ensure the safe and efficient navigation of SMRVs through dynamic traffic environments, considering factors, such as safety distance, lane-changing trajectory planning, and multivehicle coordination. Finally, a collision resolution and avoidance strategy is proposed for SMRVs at intersections, leveraging the vehicle-infrastructure cooperative environment and the AIoT framework to minimize the risk of collisions and optimize the intersection crossing process. The experimental results demonstrate that the proposed AIoT-LC method significantly outperforms existing approaches, reducing average travel time for SMRVs by up to 25%, improving average speed by 15%, and increasing the success rate of emergency responses by 20% points compared to the best performing baseline method. Additionally, the proposed method reduces fuel consumption by 11.7% and CO2 emissions by 11.7% while improving overall traffic flow efficiency by 8.9%.