Intelligent and connected vehicles have leveraged edge computing paradigm to enhance their environment comprehension and behavior planning capabilities. As the quantity of intelligent vehicles and the demand for edge computing are increas-ing rapidly, it becomes critical to efficiently orches-trate the communication and computation resources on edge clouds. Existing methods usually perform resource allocation in a fairly effective but still reac-tive manner, which is subject to the capacity of nearby edge clouds. To deal with the contradiction between the spatiotemporally varying demands for edge com-puting and the fixed edge cloud capacity, we proac-tively balance the edge computing demands across edge clouds by appropriate route planning. In this pa-per, route planning and resource allocation are jointly optimized to enhance intelligent driving. We pro-pose a multi-scale decentralized optimization method to deal with the curse of dimensionality. In large-scale optimization, backpressure algorithm is used to con-duct route planning and load balancing across edge clouds. In small-scale optimization, game-theoretic multi-agent learning is exploited to perform regional resource allocation. The experimental results show that the proposed algorithm outperforms the base-line algorithms which optimize route planning and re-source allocation separately.