Shiyao Sang

Shiyao Sang, Yinggang Ling

Robotic middleware is fundamental to ensuring reliable communication among system components and is crucial for intelligent robotics, autonomous vehicles, and smart manufacturing. However, existing robotic middleware often struggles to meet the diverse communication demands, optimize data transmission efficiency, and maintain scheduling determinism between Orin computing units in large-scale L4 autonomous vehicle deployments. This paper presents RIMAOS2C, a service discovery-based hybrid network communication middleware designed to tackle these challenges. By leveraging multi-level service discovery multicast, RIMAOS2C supports a wide variety of communication modes, including multiple cross-chip Ethernet protocols and PCIe communication capabilities. Its core mechanism, the Message Bridge, optimizes data flow forwarding and employs shared memory for centralized message distribution, reducing message redundancy and minimizing transmission delay uncertainty. Tested on L4 vehicles and Jetson Orin domain controllers, RIMAOS2C leverages TCP-based ZeroMQ to overcome the large-message transmission bottleneck in native CyberRT. In scenarios with two cross-chip subscribers, it eliminates message redundancy and improves large-data transmission efficiency by 36 to 40 percent while reducing callback latency variation by 42 to 906 percent. This research advances the communication capabilities of robotic operating systems and proposes a novel approach to optimizing communication in distributed computing architectures for autonomous driving.

Shiyao Sang

We challenge the long-standing assumption that exhaustive scene modeling is required for high-performance end-to-end autonomous driving (E2EAD). Inspired by cognitive science, we propose that effective planning arises not from reconstructing the world, but from the co-evolution of belief and intent within a minimal set of semantically rich tokens. Experiments on the nuPlan benchmark (720 scenarios, 11k+ samples) reveal three principles: (1) sparse intent tokens alone achieve 0.487 m ADE, demonstrating strong performance without future prediction; (2) conditioning trajectory decoding on predicted future tokens reduces ADE to 0.382 m, a 21.6% improvement, showing that performance emerges from cognitive planning; and (3) explicit reconstruction loss degrades performance, confirming that task-driven belief-intent co-evolution suffices under reliable perception inputs. Crucially, we observe the emergence of cognitive consistency: through prolonged training, the model spontaneously develops stable token dynamics that balance current perception (belief) and future goals (intent). This process, accompanied by "temporal fuzziness," enables robustness under uncertainty and continuous self-optimization. Our work establishes a new paradigm: intelligence lies not in pixel fidelity, but in the tokenized duality of belief and intent. By reframing planning as understanding rather than reaction, TIWM bridges the gap between world models and VLA systems, paving the way for foresightful agents that plan through imagination. Note: Numerical comparisons with methods reporting results on nuScenes are indicative only, as nuPlan presents a more challenging planning-focused evaluation.