3 citations · 6 across the 10 of their papers we have counts for
10 papers
Safe Overtaking for Autonomous Racing Using Hierarchical Optimization and Learning-Based Control
Hassan Jardali, Kai Yin, Lantao Liu
Autonomous racing overtaking requires balancing competitive performance with safety under nonlinear vehicle dynamics and real-time constraints. Model Predictive Control (MPC) combi…
Boundary-Aware Value Function Generation for Safe Stochastic Motion Planning
Junhong Xu, Kai Yin, Jason M. Gregory +2
Navigation safety is critical for many autonomous systems such as self-driving vehicles in an urban environment. It requires an explicit consideration of boundary constraints that…
Kernel-based diffusion approximated Markov decision processes for autonomous navigation and control on unstructured terrains
Junhong Xu, Kai Yin, Zheng Chen +3
We propose a diffusion approximation method to the continuous-state Markov Decision Processes (MDPs) that can be utilized to address autonomous navigation and control in unstructur…
Bayesian Uncertainty Quantification of Local Volatility Model
Kai Yin, Anirban Mondal
Local volatility is an important quantity in option pricing, portfolio hedging, and risk management. It is not directly observable from the market; hence calibrations of local vola…
A Two Stage Adaptive Metropolis Algorithm
Anirban Mondal, Kai Yin, Abhijit Mandal
We propose a new sampling algorithm combining two quite powerful ideas in the Markov chain Monte Carlo literature -- adaptive Metropolis sampler and two-stage Metropolis-Hastings s…
Online Planning in Uncertain and Dynamic Environment in the Presence of Multiple Mobile Vehicles
Junhong Xu, Kai Yin, Lantao Liu
We investigate the autonomous navigation of a mobile robot in the presence of other moving vehicles under time-varying uncertain environmental disturbances. We first predict the fu…
Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision Processes
Junhong Xu, Kai Yin, Lantao Liu
We propose a principled kernel-based policy iteration algorithm to solve the continuous-state Markov Decision Processes (MDPs). In contrast to most decision-theoretic planning fram…
Reachable Space Characterization of Markov Decision Processes with Time Variability
Junhong Xu, Kai Yin, Lantao Liu
We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address decision-theoretic planning problems for autonomous systems operating in u…
State-Continuity Approximation of Markov Decision Processes via Finite Element Methods for Autonomous System Planning
Junhong Xu, Kai Yin, Lantao Liu
Motion planning under uncertainty for an autonomous system can be formulated as a Markov Decision Process with a continuous state space. In this paper, we propose a novel solution…
A Spatio-Temporal Representation for the Orienteering Problem with Time-Varying Profits
Zhibei Ma, Kai Yin, Lantao Liu +1
We consider an orienteering problem (OP) where an agent needs to visit a series (possibly a subset) of depots, from which the maximal accumulated profits are desired within given l…