Extended object tracking is essential yet challenging in Advanced Driver Assistance Systems (ADAS) development. This paper introduces a novel hybrid filtering framework designed to integrate low-, mid-, and high-level features into a unified box representation of tracked objects. The framework provides a structured approach to multi-level fusion, ensuring consistent and accurate state estimation across different sensor inputs. To achieve this, we incorporate Probabilistic Multi-Hypothesis Tracking (PMHT) for low-level data and a Box-to-Box update model for mid- and high-level features. Comprehensive validation using high-fidelity simulated scenarios demonstrates the framework's ability to enhance tracking accuracy and robustness by leveraging structured sensor representations, closely reflecting real-world conditions.

State-of-the-art approaches for autonomous driving integrate multiple sub-tasks of the overall driving task into a single pipeline that can be trained in an end-to-end fashion by passing latent representations between the different modules. In contrast to previous approaches that rely on a unified grid to represent the belief state of the scene, we propose dedicated representations to disentangle dynamic agents and static scene elements. This allows us to explicitly compensate for the effect of both ego and object motion between consecutive time steps and to flexibly propagate the belief state through time. Furthermore, dynamic objects can not only attend to the input camera images, but also directly benefit from the inferred static scene structure via a novel dynamic-static cross-attention. Extensive experiments on the challenging nuScenes benchmark demonstrate the benefits of the proposed dual-stream design, especially for modelling highly dynamic agents in the scene, and highlight the improved temporal consistency of our approach. Our method titled DualAD not only outperforms independently trained single-task networks, but also improves over previous state-of-the-art end-to-end models by a large margin on all tasks along the functional chain of driving.

Developing quality of life technologies (QoLT) poses unique challenges to engineers and computer scientists. This paper provides a roadmap for design, development, and evaluation. We begin with a definition of quality of life and identify key attributes of QoLT. This is followed by a discussion of methods for identifying target populations and needs. Next, we describe the process of user-centered iterative design and development and conclude with evaluation strategies to assess the impact of QoLT. A central theme of the paper is that successful technology development in this arena is highly dependent on collaborative interdisciplinary teams involving social scientists, clinicians, engineers, and computer scientists.