3 papers
cs.RO2022
Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Experts
Stefano Pini, Christian S. Perone, Aayush Ahuja +3
The goal of autonomous vehicles is to navigate public roads safely and comfortably. To enforce safety, traditional planning approaches rely on handcrafted rules to generate traject…
cs.LG2022
CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization
Eesha Kumar, Yiming Zhang, Stefano Pini +4
The imitation learning of self-driving vehicle policies through behavioral cloning is often carried out in an open-loop fashion, ignoring the effect of actions to future states. Tr…
cs.RO2021
SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
Matt Vitelli, Yan Chang, Yawei Ye +7
In this paper we present the first safe system for full control of self-driving vehicles trained from human demonstrations and deployed in challenging, real-world, urban environmen…