  Mobile edge computing (MEC) is a promising technology to support
mission-critical vehicular applications, such as intelligent path planning and
safety applications. In this paper, a collaborative edge computing framework is
developed to reduce the computing service latency and improve service
reliability for vehicular networks. First, a task partition and scheduling
algorithm (TPSA) is proposed to decide the workload allocation and schedule the
execution order of the tasks offloaded to the edge servers given a computation
offloading strategy. Second, an artificial intelligence (AI) based
collaborative computing approach is developed to determine the task offloading,
computing, and result delivery policy for vehicles. Specifically, the
offloading and computing problem is formulated as a Markov decision process. A
deep reinforcement learning technique, i.e., deep deterministic policy
gradient, is adopted to find the optimal solution in a complex urban
transportation network. By our approach, the service cost, which includes
computing service latency and service failure penalty, can be minimized via the
optimal workload assignment and server selection in collaborative computing.
Simulation results show that the proposed AI-based collaborative computing
approach can adapt to a highly dynamic environment with outstanding
performance.
