After more than a decade of intense focus on automated vehicles, we are still facing huge challenges for the vision of fully autonomous driving to become a reality. The same "disillusionment" is true in many other domains, in which autonomous Cyber-Physical Systems (CPS) could considerably help to overcome societal challenges and be highly beneficial to society and individuals. Taking the automotive domain, i.e. highly automated vehicles (HAV), as an example, this paper sets out to summarize the major challenges that are still to overcome for achieving safe, secure, reliable and trustworthy highly automated resp. autonomous CPS. We constrain ourselves to technical challenges, acknowledging the importance of (legal) regulations, certification, standardization, ethics, and societal acceptance, to name but a few, without delving deeper into them as this is beyond the scope of this paper. Four challenges have been identified as being the main obstacles to realizing HAV: Realization of continuous, post-deployment systems improvement, handling of uncertainties and incomplete information, verification of HAV with machine learning components, and prediction. Each of these challenges is described in detail, including sub-challenges and, where appropriate, possible approaches to overcome them. By working together in a common effort between industry and academy and focusing on these challenges, the authors hope to contribute to overcome the "disillusionment" for realizing HAV.
翻译:在对自动化车辆的高度关注超过十年之后,我们仍面临着完全自主驱动成为现实这一愿景的巨大挑战。同样“失望”在许多其他领域也是如此,在这些领域中,自主的网络物理系统可以大大帮助克服社会挑战,对社会和个人非常有益。以汽车领域,即高度自动化的车辆(HAV)为例,本文件总结了在实现安全、可靠、可靠和可信赖的高度自动化的重塑方面仍有待克服的重大挑战。我们面对技术挑战加以约束,承认(法律)规章、认证、标准化、伦理和社会认可的重要性,承认(法律)规章、认证、标准化、伦理和社会认可的重要性,仅列举几个领域,而不必深入探讨这些挑战,因为这超出了本文件的范围。有四个挑战被确定为实现HAV的主要障碍:实现连续、部署后系统改进、处理不确定性和不完整信息、用机器学习组件核查HAV,以及预测。我们详细描述了这些挑战中的每一项,包括分挑,承认(认证、标准化、伦理、伦理和社会认可)名称的重要性,而无需深入探讨,因为这样做超出了本文件的范围。我们发现,四大挑战是实现HAV的主要障碍:实现连续、部署后系统改进、处理不确定性和不完整的信息,用机器学习部件进行核查,以及预测。我们详细描述了如何应对这些挑战,努力,共同克服挑战,努力,共同克服挑战。