The field of autonomous perception and localization is rapidly advancing, with a focus on improving the accuracy and robustness of multi-object tracking, visual odometry, and object pose estimation. Recent developments have highlighted the importance of considering ego-vehicle speed, dynamic objects, and uncertainty modeling in these tasks. The integration of machine learning techniques, such as learnable Kalman filtering and transformer-based architectures, has shown significant promise in enhancing the performance of these systems. Noteworthy papers in this area include Stable at Any Speed, which proposes a speed-guided learnable Kalman filter for multi-object tracking, and CoProU-VO, which introduces a novel approach to combining projected uncertainty for end-to-end unsupervised monocular visual odometry. Additionally, MVTOP presents a holistic multi-view approach to object pose estimation, demonstrating impressive results on synthetic and real-world datasets. Other notable works include Occupancy Learning with Spatiotemporal Memory, which proposes a scene-level occupancy representation learning framework, and Cross-View Localization via Redundant Sliced Observations and A-Contrario Validation, which introduces a two-stage method for cross-view localization with redundant observations and reliability validation.