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.
FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles
Department of Computer Science and Information Systems, BITS Pilani, Pilani Campus（计算机科学与信息系统系，比斯汉学院）；Department of Systems and Computer Engineering, Carleton University（系统与计算机工程系，卡尔顿大学）
