Advanced intersection control systems have been created to alleviate traffic congestion. CAVs may benefit from cooperative navigation in order to address the regular traffic issue. Due to whole uncertainty in transportation network, the conventional motion planning for local areas may lead to undesirable effects in long run. In prior works, a micro–macro flow control (MiMaFC) strategy is used to investigate CAVs' navigation at unsignalised intersections by taking flow velocity and vehicle passing priority into account. To get a better understanding of motion control and how it can be utilised to impact traffic flow behaviour, this study expands the intersection navigation protocol and develops a velocity planning methodology based on the proposed MiMaFC technology. Correspondingly, cooperative navigation protocol used in the addressed architecture is specifically developed for CAVs that continually cross intersections. Further, spatio‐temporal velocity adaption mechanism is presented in this work. Depending on the vehicles' location and speed, CAVs might use either the MiMaFC‐based or self‐interested velocity strategy. Simulation results, which include a congested traffic network, are shown to demonstrate the proposed method's potential. The study found that the suggested motion planning framework may increase urban network mobility over a non‐supervised CAVs system.

Autonomous cleaning robots operating in semi-structured dynamic environments must execute task-oriented motions while safely interacting with surrounding agents. These agents include pedestrians, vehicles, and other robots. In such environments (e.g., interaction-rich campus environments), reliable self-decision-making requires anticipating the future motions of surrounding agents rather than relying solely on reactive obstacle avoidance. This paper presents a trajectory prediction-enabled self-decision-making framework for autonomous cleaning robots in campus environments. A learning-based multi-agent trajectory prediction model is trained offline using public benchmarks and real-world operational data to capture typical interaction patterns in corridor-following, edge-cleaning, and intersection scenarios. The predicted trajectories are then incorporated as forward-looking priors into the robot’s online decision-making and planning process, enabling prediction-aware yielding, detouring, and task continuation decisions. The proposed framework is evaluated using real-world data-driven scenario reconstruction on a high-fidelity simulation platform that incorporates realistic vehicle dynamics and heterogeneous traffic participants. This evaluation focuses on short-horizon prediction performance and its impact on downstream decision-making stability. The results show that integrating trajectory prediction into the decision-making loop leads to more stable motion behavior and fewer abrupt adjustments in interaction scenarios. Under short-term prediction horizons, the evaluation results show that the proposed model achieves ADERate and FDERate exceeding 90% under predefined error thresholds, while lane-change prediction accuracy remains around 79%. In addition, the robot maintains stable speed tracking with only minor fluctuations under medium-density traffic conditions.