Modeling and calibrating the fidelity of synthetic data is paramount in
shaping the future of safe and reliable self-driving technology by offering a
cost-effective and scalable alternative to real-world data collection. We focus
on its role in safety-critical applications, introducing four types of
instance-level fidelity that go beyond mere visual input characteristics. The
aim is to align synthetic data with real-world safety issues. We suggest an
optimization method to refine the synthetic data generator, reducing fidelity
gaps identified by the DNN-based component. Our findings show this tuning
enhances the correlation between safety-critical errors in synthetic and real
images.

通过模拟和校准合成数据的逼真度，可以为安全可靠的自动驾驶技术的未来塑造提供一种经济实惠且可扩展的替代方案，从而避免实际数据采集的高成本。我们着重关注其在安全关键应用中的作用，引入四种超越单纯视觉输入特性的实例级逼真度。我们提出了一种优化方法来改进合成数据生成器，减少由 DNN 组件识别出的逼真度差别。我们的研究结果表明，这种调整可以增强合成和真实图像之间的安全关键错误的相关性。

合成数据的实例级安全感知逼真度及其校准

Instance-Level Safety-Aware Fidelity of Synthetic Data and Its  Calibration

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。