CAVs as a Mobile Computing Platform: Task Offloading Strategy in Mixed Traffic Systems

With the proliferation of connected and automated vehicles (CAVs), densely distributed edge computing nodes have emerged on roadways. Consequently, leveraging CAVs as a mobile computing platform can integrate idle vehicle resources to provide computational services for ubiquitous Internet of Things (IoT) devices. Numerous studies have investigated task offloading strategy in the systems with full CAVs penetration. It is expected that the coexistence of CAVs and human-driven vehicles (HDVs) in mixed traffic systems will continue for a considerable period. However, due to the impact of HDVs on communication performance, the task offloading model designed for the systems with full CAVs penetration are no longer applicable in mixed traffic systems. We explore task offloading schemes using CAVs as a mobile computing platform in mixed traffic systems to address this issue. Specifically, we first model the communication model in mixed traffic systems, taking into account the influence of HDVs on link interference, the alteration of path loss due to the impact of HDVs on routing, and the additional sensing tasks arising from the inability of HDVs and CAVs to communicate. Subsequently, considering that delay and energy consumption are crucial factors affecting the performance of CAVs as a mobile computing platform, we formulate the task offloading scheme as an optimization problem. Additionally, we employ a distributed offloading based on deep learning (DODL) algorithm to obtain approximately optimal offloading decisions. Simulation results demonstrate the effectiveness of the proposed model in mixed traffic systems. By employing the DODL algorithm, the CAVs as a mobile computing platform can achieve enhanced performance in terms of convergence, thereby advancing the development of autonomous driving in mixed traffic systems.