With the rapid development of autonomous driving technology, future road traffic must be composed of autonomous vehicles and artificial vehicles. Although autonomous vehicles have greatly improved road capacity, few studies have involved capacity at signal-controlled intersections, and most of the studies are based on experimental simulation. As such, there is a need to more scientifically analyze the impact of autonomous vehicles on road and intersection capacity. Based on three theories of flow-density relationships, traffic flow equilibrium analysis, and the following model, this paper firstly deduces the flow-density relationship of different vehicle types in a single environment. Secondly, flow-density relationships under different proportions of self-driving vehicles are derived. Through the derivation of these two models, the basic road saturation flow rates under different permeabilities of self-driving vehicles, can be obtained. Based on these results, a revised calculation model for the capacity of signalized intersections with different proportions of autonomous vehicles is proposed, which is essentially to revise the basic saturation flow rate under different permeabilities of autonomous vehicles. By using SUMO 1.15.0 traffic simulation software, the theoretical models are individually tested. The results show that the error rate between the theoretical calculation results and the SUMO simulation results, is less than 16%. This study can provide a basis for the calculation of basic capacity of roads and intersections in a future man-machine hybrid driving environment, and provide theoretical guidance for traffic management and control.

High-precision, long-endurance navigation remains a central bottleneck for autonomous underwater vehicles (AUVs) operating in GNSS-denied, acoustically constrained, and dynamically disturbed marine environments. This manuscript examines the complete sensing-mapping-estimation chain for underwater geomagnetic navigation. It distinguishes scalar and vector measurements; compares shipborne, towed, and AUV-mounted survey configurations, calibration requirements, platform-interference mitigation, and uncertainty sources; reviews global, regional, and local magnetic models; and evaluates nonlinear map-aided positioning. Existing approaches are organized into map-based matching, filter-aided navigation, geomagnetic simultaneous localization and mapping (SLAM), and matching-area adaptability assessment. Their assumptions, data requirements, uncertainty treatment, accuracy evidence, and computational burden are critically compared. Persistent gaps include magnetic cleanliness, three-dimensional mapping, weak-feature-area observability, benchmark datasets, uncertainty quantification, and reproducible long-duration sea trials.