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 introduces a [occlusion-aware contingency safety-critical planning approach](https://www.linkedin.com/feed/update/urn:li:activity:7297822689702920192/) for safety-critical autonomous vehicles. Leveraging reachability analysis for risk assessment, forward reachable sets of occluded phantom obstacles are computed. The occlusion-aware contingency planner then constructs multiple locally optimal trajectory branches (each tailored to [different risk scenarios](https://www.linkedin.com/feed/update/urn:li:activity:7304187070090985473/)), and a shared consensus trunk is generated to ensure smooth transitions and motion consistency.
This research introduces a consistent parallel trajectory optimization (CPTO) approach for real-time, consistent, and safe trajectory planning for autonomous driving in partially observed environments. The CPTO framework introduces a consensus safety barrier module, ensuring that each generated trajectory maintains a consistent and safe segment, even when faced with varying levels of obstacle detection accuracy. We validate our CPTO framework through extensive comparisons with state-of-the-art baselines across multiple driving tasks in partially observable environments. [Our results demonstrate improved safety and consistency using both synthetic and real-world traffic datasets.](https://youtu.be/YAdtW7J75SY)
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.
Enforcing safety while preventing overly conservative behaviors is essential for autonomous vehicles to achieve high task performance. In this project, we propose a barrier-enhanced homotopic parallel trajectory optimization approach with over-relaxed alternating direction method of multipliers for real-time integrated decision-making and planning in cluttered driving environments. [Through a series of experiments, the proposed development demonstrates improved task accuracy, stability, and consistency in various traffic scenarios using synthetic and real-world traffic datasets.](https://www.youtube.com/watch?v=6pVSoaKdr3M)
Present an integrated decision and trajectory planning approach for the autonomous vehicles to achieve high travel efficiency in dynamic and congested environments.
This project presents a cutting-edge approach for safe and efficient autonomous driving in dense traffic scenarios. Our proposed Spatiotemporal Receding Horizon Control (ST-RHC) scheme generates dynamically feasible and energy-efficient trajectories in real-time, enabling vehicles to accurately perform complex driving tasks. The algorithm employs receding horizon optimization and iterative parallel methods to design a trajectory tree that optimizes planning and ensures proactive interaction to avoid accidents. We have implemented our algorithms on an autonomous car, successfully achieving vehicle following, lane changing, overtaking, and cruise driving in dense traffic flow simulations based on ROS2.
Once received return control signal, the drone must generate smooth trajectories in real-time to avoid collision and be close to the reference spraying path to realize [high-performance precision spraying]( https://www.youtube.com/watch?v=Phy0X5KdiLk) in precision farming.
We propose a learning-based tracking control scheme based on a feedback linearization controller in which uncertain disturbances are approximated online using Gaussian Processes. Using the predicted distribution of disturbances given by GPs, a Control Lyapunov Function and Control Barrier Function based Quadratic Program is applied, with which probabilistic stability and safety are guaranteed.