This paper investigates the obstacle avoidance problem of autonomous vehicles in campus environment, and proposes a path planning algorithm based on OpenPlanner and hybrid A*. In the global planning stage, OpenPlanner’s global planner quickly plans a global path from the starting point to the destination using a vector map. In the local planning phase, OpenPlanner’s local planner samples and generates local trajectory clusters, while the hybrid A* dynamically adjusts the path based on the cost map to ensure the feasibility and safety of the path. In order to analyze the performance of the proposed planning scheme, an Autoware-based campus unmanned vehicle autopilot platform is built. The experimental results show that the unmanned vehicle successfully drives through turning intersections, complex road sections, and crossroads, and completes planning tasks such as obstacle avoidance, stopping, and cruising. Meanwhile, the success rate of obstacle avoidance is proved to be 96.67% through testing. The proposed path planning scheme can be used for automatic driving of unmanned vehicles on campus.