Driving everywhere with large language model policy adaptation
Adapting driving behavior to new environments customs and laws is a long-standing
problem in autonomous driving precluding the widespread deployment of autonomous …
problem in autonomous driving precluding the widespread deployment of autonomous …
RuleFuser: Injecting Rules in Evidential Networks for Robust Out-of-Distribution Trajectory Prediction
Modern neural trajectory predictors in autonomous driving are developed using imitation
learning (IL) from driving logs. Although IL benefits from its ability to glean nuanced and …
learning (IL) from driving logs. Although IL benefits from its ability to glean nuanced and …
STLCG++: A Masking Approach for Differentiable Signal Temporal Logic Specification
Signal Temporal Logic (STL) offers a concise yet expressive framework for specifying and
reasoning about spatio-temporal behaviors of robotic systems. Attractively, STL admits the …
reasoning about spatio-temporal behaviors of robotic systems. Attractively, STL admits the …
[PDF][PDF] Driving Everywhere with Large Language Model Policy Adaptation
Adapting driving behavior to new environments, customs, and laws is a long-standing
problem in autonomous driving, precluding the widespread deployment of autonomous …
problem in autonomous driving, precluding the widespread deployment of autonomous …
[PDF][PDF] D4. 1 Initial version of motion planning and behavioural decision-making components
WP All, FT CRF - 2022 - researchgate.net
This document reports the initial work done for Tasks 4.1 (Motion Planning) and 4.2
(Behavioural decision making) of the EVENTS project. It contains the preliminary designs of …
(Behavioural decision making) of the EVENTS project. It contains the preliminary designs of …