Intelligent vehicles have increased their capabilities for highly and, even fully, automated
driving under controlled environments. Scene information is received using onboard …

Motion planning is a fundamental research area in robotics. Sampling-based methods offer
an efficient solution for what is otherwise a rather challenging dilemma of path planning …

We propose a framework for planning in unknown dynamic environments with probabilistic
safety guarantees using conformal prediction. Particularly, we design a model predictive …

The prospect of driverless cars wide-scale deployment is imminent owing to the advances in
robotics, computational power, communications, and sensor technologies. This promises …

This paper presents a real-time path planning algorithm that guarantees probabilistic
feasibility for autonomous robots with uncertain dynamics operating amidst one or more …

Rapidly-exploring Random Trees (RRTs) are successful in single-query motion planning
problems. The standard version of RRT grows a tree from a start location and stops once it …

This paper reports on an integrated inference and decision-making approach for
autonomous driving that models vehicle behavior for both our vehicle and nearby vehicles …

This paper presents an intention-aware online planning approach for autonomous driving
amid many pedestrians. To drive near pedestrians safely, efficiently, and smoothly …

As robots venture into new application domains as autonomous vehicles on the road or as
domestic helpers at home, they must recognize human intentions and behaviors in order to …

The computationally efficient search for robust feasible paths for unmanned aerial vehicles
(UAVs) in the presence of uncertainty is a challenging and interesting area of research. In …