This paper presents a novel occlusion-aware Consistent Model Predictive Control (CMPC) strategy for mobile robots navigating in environments with occluded obstacles — situations where obstacles are hidden from the robot's field of view but pose potential collision risks. The method strikes an effective balance between safety (avoiding collisions with occluded obstacles) and performance (maintaining smooth, consistent motion), enabling robots to navigate confidently in cluttered, partially observable environments.
Human driver unpredictability forces a false choice between excessive caution and collision risk. We introduce event-triggered online learning that dynamically refines behavior estimates, adjusting safety margins based on observed actions. Our dual-trajectory framework with FRS-based barriers achieves zero collisions, 64% lower lateral jerk, and 14-20% higher speeds than conservative approaches—proving safety and efficiency can coexist without compromise.
Autonomous vehicles navigating occluded intersections face unseen hazards. Our solution co-optimizes exploration and safety-fallback trajectories with a shared initial segment, enabling smooth transitions. Using reachability-based risk assessment and consensus ADMM, we achieve 30% faster traversal, non-stop motion, and 90% faster computation than baselines—validated on real robotic platforms.
Real-time safe trajectory planning for autonomous vehicles in occluded environments is achieved through a contingency planner that combines reachability analysis with consensus ADMM optimization, validating improved safety and efficiency in dynamic intersections.
This framework introduces a consensus spatiotemporal safety barrier within a scenario tree structure to address potential hazards. Using consensus ADMM and a shared spatiotemporal safety barrier for each trajectory, the framework evaluates diverse risk configurations while enforcing motion consistency under perception uncertainty, such as sensor mis-detections. The framework is particularly effective in environments with dense obstacles in partially observable environments, enabling scalability to handle an increasing number of obstacles in large-scale, real-time optimization. We present the limitations of current parallel trajectory planning approaches, particularly their low accuracy and poor real-time performance.
One key underlying factor to the safety concern stems from uncertainties in both internal system models and external environments. [These uncertainties become critical in contingency scenarios, where potential risks cannot be predicted with certainty](https://iscicra25.github.io).
This research seamlessly integrates discrete decision-making maneuvers with continuous trajectory variables for safety-critical autonomous driving. The algorithm operates in real-time, optimizing trajectories of autonomous vehicles to ensure safety, stability, and proactive interaction with uncertain human-driven vehicles across various driving tasks, utilizing over-relaxed ADMM iterations. We provide a comprehensive theoretical analysis of safety and computational efficiency.
This research proposes a computationally-efficient spatiotemporal receding horizon control (ST-RHC) scheme to generate a safe, dynamically feasible, energy-efficient trajectory in control space, where different driving tasks in dense traffic can be achieved with high accuracy and safety in real time. The effectiveness of the proposed ST-RHC scheme is demonstrated through comprehensive comparisons with state-of-the-art algorithms on synthetic and real-world traffic datasets under dense traffic.
Present a real-time fail-operational controller for autonomous driving in the presence of time-varying environmental disturbances. This controller is designed to guide autonomous vehicles back to a predefined safe state asymptotically, while upholding task efficiency.
Present an integrated decision and trajectory planning approach for the autonomous vehicles to achieve high travel efficiency in dynamic and congested environments.