Vehicle-Edge Collaborative Intelligent Driving Task Processing With Task Complexity and Service Availability Awareness

As wireless communication technology advances, the convergence of the Internet of Vehicles (IoV) and edge computing is increasingly underpinning the progression of intelligent driving systems. Despite these advances, challenges persist in the stability and efficiency of vehicle-edge collaborative computing frameworks. This study introduces a service availability and task complexity-aware scheduling framework for intelligent driving tasks (SATCAS-IDTs), ensuring the reliability of task scheduling between user vehicles and in-vehicle computing platforms (IVCPs) during high-speed mobility. Initially, a task complexity quantification model for intelligent driving is constructed, incorporating metrics such as information entropy, image contrast, energy, and color space. Subsequently, a vehicle mobility model tailored for high-speed multilane environments is designed, upon which the mobility and service availability aware IVCP initial screening algorithm (MSAIF-IVCP) is developed, preselecting IVCPs that meet mobility and service availability requirements. Lastly, the double deep recurrent reinforcement learning task scheduling algorithm with multiobservation hybrid encoder (DDRRL-MOHE) is proposed. By intelligently balancing task delay and processing accuracy, this algorithm dynamically adapts edge computing resources, effectively reducing response times and enhancing overall system performance. Experimental validation shows that our proposed method improves the task processing accuracy by about 1.70% on average and reduces the average latency by about 1.97% over the baseline method.