Wang, Shan2026-04-292026-04-29https://hdl.handle.net/1885/733808736Autonomous driving systems require robust visual perception to operate safely in diverse and dynamic environments. This thesis advances visual perception by focusing on two key areas: cross-view localisation and temporal fusion, aiming to reduce reliance on expensive sensors and pre-built 3D maps while achieving accurate and reliable performance. To overcome the limitations of traditional localisation methods, which often depend on costly survey-grade mapping vehicles, we propose a scalable alternative that leverages high-resolution satellite imagery as a global map. By aligning ground-view images from vehicle-mounted cameras with aerial views, we achieve accurate cross-view localisation using only visual inputs. We introduce three novel localisation solutions: (1) A visual-LiDAR hybrid method that uses 3D LiDAR points to establish correspondences between views, featuring a geometric-aligned feature extractor and iterative pose refinement. (2) A purely visual method that detects view-consistent ground keypoints and exploits spatial camera constraints to suppress dynamic and seasonal noise. (3) An enhanced visual method that aggregates off-ground aerial features onto ground-level pixels via orthogonal-view transformation, improving alignment and orientation estimation. Beyond localisation, we tackle the challenge of occluded lane markings through a homography-guided temporal fusion module. By combining information from adjacent video frames with a surface-normal estimator, this approach improves lane segmentation under visual occlusions while remaining computationally efficient. Together, these contributions form a robust visual perception framework. By reducing dependency on external sensors and pre-built maps, our methods enable autonomous systems to achieve accurate, reliable, and scalable perception in real-world conditions.en-AU202610.25911/Z9H4-XQ61