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)
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)
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.
A learning-based safety-stability-driven control algorithm is presented to guarantee the safety and tracking stability for nonlinear safety-critical systems subject to control input constraints under model uncertainties.
For safety-critical vehicles in mixed traffic flow where most vehicles are human-driven, each autonomous vehicle keeps tracking its front vehicle at a desired constant speed while maintaining a safe following distance with it in normal situations. However, when a vehicle decelerates urgently in unexpected situations, the vehicle behind has to reduce its speed to avoid collision with the front vehicle. In these cases, there exists a conflict between safety and stable high-performance tracking. For safety-critical autonomous vehicles, safety must not be violated and the tracking errors should be kept as small as possible.