Collaborative Vehicular Perception (CVP) enables connected and autonomous vehicles (CAVs) to cooperatively extend their views through wirelessly sharing their sensor data. Existing CVP systems employ either a vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) view exchange paradigm. In this paper, we advocate a hybrid CVP design: our developed system, Harbor, employs V2I as its fundamental underlying framework, and opportunistically employs V2V to boost the performance. In Harbor, vehicles (helpers) may serve as relays to assist other vehicles (helpees) in reaching an edge node, which performs sensor data merging to produce the extended view. We judiciously partition the workload between the edge and vehicles, develop a robust helper-helpee assignment model, and solve it efficiently at runtime. We conduct both real-world tests and large-scale emulation experiments using two prevailing CAV applications: drivable space detection and object detection. Our real-world evaluation conducted at one of the world’s first purpose-built autonomous driving testbeds demonstrates that Harbor outperforms state-of-the-art V2V- or V2I-only CVP schemes by up to 36% in detection accuracy, resulting in significantly fewer collisions under dangerous driving scenarios.