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Tunnels on freeways, as one of the critical bottlenecks, frequently cause severe congestion and passenger delay. To solve the tunnel bottleneck problem, most of the existing research can be divided into two types. One is to adopt variable speed limits (VSLs) to regulate a predetermined speed for vehicles to get through a bottleneck smoothly. The other is to adopt high-occupancy vehicle (HOV) lane management. In HOV lane management strategies, all traffic is divided into HOVs and low-occupancy vehicles (LOVs). HOVs are vehicles with a driver and one or more passengers. LOVs are vehicles with only a driver. This kind of research can grant priority to HOVs by providing a dedicated HOV lane. However, the existing research cannot both mitigate congestion and maximize passenger-oriented benefits. To address the research gap, this paper leverages connected and automated vehicle (CAV) technologies on intelligent freeways and develops a tunnel bottleneck management strategy with a dynamic HOV lane (DHL). The strategy bears the following features: 1) enables tunnel bottleneck management at a microscopic level; 2) maximizes passenger-oriented benefits; 3) grants priority to HOVs even when the HOV lane is open to LOVs; 4) allocates right-of-way segments for HOVs and LOVs in real time; and 5) performs well in a mixed-traffic environment. The proposed strategy is evaluated through comparison against the non-control baseline and a VSL strategy. Sensitivity analysis is conducted under different congestion levels and penetration rates. The results demonstrate that the proposed strategy outperforms in terms of passenger-oriented delay reduction and HOVs' priority level improvement. [ABSTRACT FROM AUTHOR]