Semantic occupancy has emerged as a powerful representation in world models for its ability to capture rich spatial semantics. However, most existing occupancy world models rely on static and fixed embeddings or grids, which inherently limit the flexibility of perception. Moreover, their ``in-place classification" over grids exhibits a potential misalignment with the dynamic and continuous nature of real scenarios. In this paper, we propose SparseWorld, a novel 4D occupancy world model that is flexible, adaptive, and efficient, powered by sparse and dynamic queries. We propose a Range-Adaptive Perception module, in which learnable queries are modulated by the ego vehicle states and enriched with temporal-spatial associations to enable extended-range perception. To effectively capture the dynamics of the scene, we design a State-Conditioned Forecasting module, which replaces classification-based forecasting with regression-guided formulation, precisely aligning the dynamic queries with the continuity of the 4D environment. In addition, We specifically devise a Temporal-Aware Self-Scheduling training strategy to enable smooth and efficient training. Extensive experiments demonstrate that SparseWorld achieves state-of-the-art performance across perception, forecasting, and planning tasks. Comprehensive visualizations and ablation studies further validate the advantages of SparseWorld in terms of flexibility, adaptability, and efficiency.

Automatic Emergency Braking (AEB) systems are a crucial component in ensuring the safety of passengers in autonomous vehicles. Conventional AEB systems primarily rely on closed-set perception modules to recognize traffic conditions and assess collision risks. To enhance the adaptability of AEB systems in open scenarios, we propose Dual-AEB, a system combines an advanced multimodal large language model (MLLM) for comprehensive scene understanding and a conventional rule-based rapid AEB to ensure quick response times. To the best of our knowledge, Dual-Aebis the first method to incorporate MLLMs within AEB systems. Through extensive experimentation, we have validated the effectiveness of our method. Codes will be publicly available at https://github.com/ChipsICU/Dual-AEB.

Real-time perception is important for autonomous driving. We observe an emerging trend using one large and critical fusion-based Bird’s-Eye-View (BEV) Deep Neural Network (DNN) model to perform core perception tasks. It collaborates with a few auxiliary Perspective-View (PV) models, forming a BEV-centric paradigm. Organizing the BEV and PV models respecting their distinct real-time requirements becomes challenging, especially on the state-of-the-practice GPU-integrated dual System-on-Chip (SoC) platform. It remains unclear how to appropriately allocate the separated GPU resource to BEV and PV models, satisfying their distinct real-time requirements with latency predictability. No public solution has been proposed for this emerging software-hardware combination.This paper explores parallelism and a timeslot-filling mechanism to organize tasks. We propose Jigsaw, a specialized execution timeline management framework for BEV-centric perception on dual-SoC. First, it exploits component parallelism to carefully place BEV model components and reduce BEV model latency. Second, we recognize two types of idle GPU timeslots left by a parallelized BEV model. The stable timeslot can offer hard real-time guarantee for PV models, while the unstable timeslot could only provide soft real-time capability. Therefore, Jigsaw schedules PV models by timeslot filling to ensure latency predictability of BEV model and deadline satisfaction of PV models. The framework is implemented in compliance with the practical computing stack in modern autonomous vehicles. It is evaluated on a dual-SoC prototype connected via a PCIe bus. Results show that it achieves $1.52-1.63 \times$ speedup for the BEV model compared to no parallelism. It also ensures deadline satisfaction for PV models without interference in BEV model latency predictability.