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.

该研究介绍了一种基于神经符号元强化前瞻学习（NUMERLA）的在线元强化学习算法，采用前瞻更新机制实现了自动驾驶模型在非稳态城市人车交互场景下的实时适应性和安全性。