  In the area of learning-driven artificial intelligence advancement, the
integration of machine learning (ML) into self-driving (SD) technology stands
as an impressive engineering feat. Yet, in real-world applications outside the
confines of controlled laboratory scenarios, the deployment of self-driving
technology assumes a life-critical role, necessitating heightened attention
from researchers towards both safety and efficiency. To illustrate, when a
self-driving model encounters an unfamiliar environment in real-time execution,
the focus must not solely revolve around enhancing its anticipated performance;
equal consideration must be given to ensuring its execution or real-time
adaptation maintains a requisite level of safety. This study introduces an
algorithm for online meta-reinforcement learning, employing lookahead symbolic
constraints based on \emph{Neurosymbolic Meta-Reinforcement Lookahead Learning}
(NUMERLA). NUMERLA proposes a lookahead updating mechanism that harmonizes the
efficiency of online adaptations with the overarching goal of ensuring
long-term safety. Experimental results demonstrate NUMERLA confers the
self-driving agent with the capacity for real-time adaptability, leading to
safe and self-adaptive driving under non-stationary urban human-vehicle
interaction scenarios.

Robotics;Artificial Intelligence;Systems and Control;Systems and Control;Machine Learning