The proliferation of the Internet of Vehicles (IoV) has spurred the development of computationally intensive and latency-sensitive applications in vehicles. However, limited onboard computing resources hinder their execution. Vehicle edge computing addresses this by providing readily accessible resources from edge servers. However, efficient resource allocation remains a challenge due to limited edge server capacity and dynamic traffic patterns. This paper proposes a novel three-layer collaborative intelligence framework that leverages edge and cloud resources to minimize system latency in IoV. We employ a deep reinforcement learning algorithm to adapt to dynamic traffic conditions and make optimal resource allocation decisions. Extensive experiments demonstrate a significant reduction in latency (26.75%) compared to baseline methods.