As the horizon of intelligent transportation expands with the evolution of Automated Driving Systems (ADS), ensuring paramount safety becomes more imperative than ever. Traditional risk assessment methodologies, primarily crafted for human-driven vehicles, grapple to adequately adapt to the multifaceted, evolving environments of ADS. This paper introduces a framework for real-time Dynamic Risk Assessment (DRA) in ADS, harnessing the potency of Artificial Neural Networks (ANNs). Our proposed solution transcends these limitations, drawing upon ANNs, a cornerstone of deep learning, to meticulously analyze and categorize risk dimensions using real-time On-board Sensor (OBS) data. This learning-centric approach not only elevates the ADS's situational awareness but also enriches its understanding of immediate operational contexts. By dissecting OBS data, the system is empowered to pinpoint its current risk profile, thereby enhancing safety prospects for onboard passengers and the broader traffic ecosystem. Through this framework, we chart a direction in risk assessment, bridging the conventional voids and enhancing the proficiency of ADS. By utilizing ANNs, our methodology offers a perspective, allowing ADS to adeptly navigate and react to potential risk factors, ensuring safer and more informed autonomous journeys.

翻译：随着自动驾驶系统（ADS）的演进拓展了智能交通的视野，确保最高安全性变得比以往任何时候都更为紧迫。传统风险评估方法主要针对人类驾驶车辆设计，难以充分适应ADS中多层面、动态变化的环境。本文提出了一种用于ADS实时动态风险评估（DRA）框架，利用人工神经网络（ANN）的强大能力。我们的解决方案超越了传统局限，依托深度学习基石——人工神经网络，利用实时车载传感器（OBS）数据对风险维度进行细致分析和分类。这种以学习为核心的方法不仅提升了ADS的情境感知能力，还增强了其对即时操作环境的理解。通过解析OBS数据，系统得以精准定位当前风险态势，从而提升车内乘客及更广泛交通生态系统的安全保障。借助该框架，我们开辟了风险评估的新方向，弥合了传统方法的空白，增强了ADS的效能。通过利用ANN，我们的方法提供了一种全新视角，使ADS能够灵活应对潜在风险因素，确保更安全、信息更全面的自主驾驶旅程。