Constrained NMPC under prediction uncertainty

Uncertainty in the state of obstacles during sensing and prediction has been incorporated probabilistically as chance-constraints and Barrier Functions in the optimal control problem for safe and robust planning.

Hard Constraint

Chance Constraint

CBF

Cooperative Trajectory Forecasting under Occlusion

Reliable tracking and prediction of occluded dynamic object has been achieved through an end-to-end network that performs simultaneous pose recovery and applies rigid body transformation to estimate pedestrian in occluded camera's reference which is further utilized to predict future states of the occluded object with safety guarantees.

Uncertainty-aware Trajectory Prediction

In this work, we propose an algorithm that can take uncertainty during sensing and propagate that uncertainty into the future trajectory prediction of traffic agents. The algorithm uses deep ensembles to capture both data and prediction uncertainty which can be passed downstream for robust path planning.

Cooperative Perception in connected vehicles

Established feature-based relative pose estimation between two connected vehicles to obtain the orientation and translation between vehicles. Compared feature descriptors like SIFT, SURF, ORB, KAZE, and AKAZE for efficient pose estimation.

Probabilistic RRT for path planning

Unlike the traditional RRT where samples are uniformly distributed, probabilistic-RRT induces biased sampling based on the goal and obstacle positions in time. The goal has a higher sample distribution while obstacles have very low samples. This ensures faster collision-free path towards goal.