Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services. Such a transformation—which will be fuelled by concomitant advances in technologies for machine learning (ML) and wireless communications—will enable a future vehicular ecosystem that is better featured and more efficient. However, there are lurking security problems related to the use of ML in such a critical setting where an incorrect ML decision may not only be a nuisance but can lead to loss of precious lives. In this paper, we present an in-depth overview of the various challenges associated with the application of ML in vehicular networks. In addition, we formulate the ML pipeline of CAVs and present various potential security issues associated with the adoption of ML methods. In particular, we focus on the perspective of adversarial ML attacks on CAVs and outline a solution to defend against adversarial attacks in multiple settings.

中文翻译：

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保护未来的自动驾驶和互联汽车：对抗性机器学习带来的挑战和前进的道路

联网和自动驾驶汽车 (CAV) 将成为未来下一代智能交通系统 (ITS) 的支柱，提供出行舒适性、道路安全以及多项增值服务。这种转变将伴随机器学习 (ML) 和无线通信技术的进步而推动，将使未来的车辆生态系统具有更好的功能和更高效的性能。然而，在如此关键的环境中使用 ML 存在潜在的安全问题，在这种情况下，错误的 ML 决定不仅可能令人讨厌，而且可能导致宝贵生命的损失。在本文中，我们深入概述了与 ML 在车辆网络中的应用相关的各种挑战。此外，我们制定了 CAV 的 ML 管道，并提出了与采用 ML 方法相关的各种潜在安全问题。特别是，我们关注对 CAV 的对抗性 ML 攻击的观点，并概述了在多种环境中防御对抗性攻击的解决方案。