In urban environments where satellite signals are limited, road network maps serve as structurally stable prior information that effectively suppresses the cumulative drift of visual odometry. However, existing road-network-assisted localization methods often suffer from imprecise turn detection and simplified topology-matching strategies, making them difficult to adapt to complex road structures. To address these issues, this paper proposes a road-network-assisted vehicle visual localization method integrating heading awareness and topological matching. A sliding-window strategy combined with heading-rate variation is employed to determine the vehicle motion state, while a straight-curve ratio is introduced to characterize the geometric properties of turning segments. Representative turning points are extracted using the maximum deviation measure, providing reliable structural cues for subsequent node association. Based on this, a topology matching method that accounts for both geometric consistency and structural similarity is developed. A perpendicular nearest-point constraint is applied to achieve accurate position association in straight-driving scenarios, while a multi-level topology similarity metric ensures robust structural matching during turns, significantly enhancing consistency in complex intersections, gentle curves, and high-curvature segments. Furthermore, a Kalman filter is employed to fuse short-term visual odometry observations with global structural constraints from the road network. Road-segment orientation information is utilized to suppress heading drift during straight driving, resulting in a robust localization framework that tightly couples heading perception and topology matching. Experiments conducted on the KITTI dataset and campus field tests demonstrate that the proposed method effectively suppresses the accumulated drift of visual odometry while maintaining real-time performance. The localization accuracy improves by 78.7% compared with raw visual odometry and by 38.3% and 34.0% compared with the MPF and RNAP road-network-assisted methods, respectively. Stable performance is preserved even under node deviations or partial road-network loss, confirming the reliability and general applicability of the proposed method in various complex road conditions.