142 citations · 712 across the 76 of their papers we have counts for
6 papers · 2 filters
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models
Tsun-Hsuan Wang, Alaa Maalouf, Wei Xiao +5
As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning.…
GMMap: Memory-Efficient Continuous Occupancy Map Using Gaussian Mixture Model
Peter Zhi Xuan Li, Sertac Karaman, Vivienne Sze
Energy consumption of memory accesses dominates the compute energy in energy-constrained robots which require a compact 3D map of the environment to achieve autonomy. Recent mappin…
Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving
Xiao Li, Igor Gilitschenski, Guy Rosman +2
As learning-based methods make their way from perception systems to planning/control stacks, robot control systems have started to enjoy the benefits that data-driven methods provi…
Learning When to Ask for Help: Efficient Interactive Navigation via Implicit Uncertainty Estimation
Ifueko Igbinedion, Sertac Karaman
Robots operating alongside humans often encounter unfamiliar environments that make autonomous task completion challenging. Though improving models and increasing dataset size can…
Infrastructure-based End-to-End Learning and Prevention of Driver Failure
Noam Buckman, Shiva Sreeram, Mathias Lechner +4
Intelligent intersection managers can improve safety by detecting dangerous drivers or failure modes in autonomous vehicles, warning oncoming vehicles as they approach an intersect…
Agility and Target Distribution in the Dynamic Stochastic Traveling Salesman Problem
Aviv Adler, Oren Gal, Sertac Karaman
An important variant of the classic Traveling Salesman Problem (TSP) is the Dynamic TSP, in which a system with dynamic constraints is tasked with visiting a set of n target locati…