55 citations · 74 across the 6 of their papers we have counts for
6 papers
ReasonNet: End-to-End Driving with Temporal and Global Reasoning
Hao Shao, Letian Wang, Ruobing Chen +3
The large-scale deployment of autonomous vehicles is yet to come, and one of the major remaining challenges lies in urban dense traffic scenarios. In such cases, it remains challen…
Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors
Letian Wang, Jie Liu, Hao Shao +4
When autonomous vehicles are deployed on public roads, they will encounter countless and diverse driving situations. Many manually designed driving policies are difficult to scale…
Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skills
Tong Zhou, Letian Wang, Ruobing Chen +2
Efficient and effective exploration in continuous space is a central problem in applying reinforcement learning (RL) to autonomous driving. Skills learned from expert demonstration…
Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer
Hao Shao, Letian Wang, RuoBing Chen +2
Large-scale deployment of autonomous vehicles has been continually delayed due to safety concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of wh…
Randomized Derivative-Free Optimization of Noisy Convex Functions
Ruobing Chen, Stefan Wild
We propose STARS, a randomized derivative-free algorithm for unconstrained optimization when the function evaluations are contaminated with random noise. STARS takes dynamic, noise…
Stochastic Optimization Using a Trust-Region Method and Random Models
Ruobing Chen, Matt Menickelly, Katya Scheinberg
In this paper, we propose and analyze a trust-region model-based algorithm for solving unconstrained stochastic optimization problems. Our framework utilizes random models of an ob…