The application of zero-knowledge proofs (ZKPs) in autonomous systems is an emerging area of research, motivated by the growing need for regulatory compliance, transparent auditing, and trustworthy operation in decentralized environments. zk-SNARK is a powerful cryptographic tool that allows a party (the prover) to prove to another party (the verifier) that a statement about its own internal state is true, without revealing sensitive or proprietary data about that state. This paper proposes Hermes Seal: a zk-SNARK-based ZKP framework for enabling privacy-preserving, verifiable communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) networks. The framework allows autonomous systems to generate cryptographic proofs of perception and decision-related computations without revealing proprietary models, sensor data, or internal system states, thereby supporting interoperability across heterogeneous autonomous systems. We present two real-world case studies implemented and empirically evaluated within our framework, demonstrating a step toward verifiable autonomous system information exchanges. The first demonstrates real-time proof generation and verification, achieving 8 ms proof generation and 1 ms verification on a GPU, while the second evaluates the performance of an autonomous vehicle perception stack, enabling proof of computation without exposing proprietary or confidential data. Furthermore, the framework can be integrated into AV perception stacks to facilitate verifiable interoperability and privacy-preserving cooperative perception. The demonstration code for this project is open source, available on Github.

翻译：零知识证明（ZKP）在自动驾驶系统中的应用是一个新兴的研究领域，其动力源于去中心化环境下对监管合规、透明审计和可信操作日益增长的需求。zk-SNARK是一种强大的密码学工具，允许一方（证明者）向另一方（验证者）证明关于其内部状态的某个断言为真，而无需泄露有关该状态的敏感或专有数据。本文提出Hermes Seal：一种基于zk-SNARK的ZKP框架，用于在车对车（V2V）和车对基础设施（V2I）网络中实现隐私保护、可验证的通信。该框架允许自动驾驶系统生成关于感知和决策相关计算的密码学证明，而不泄露专有模型、传感器数据或内部系统状态，从而支持异构自动驾驶系统间的互操作性。我们展示了两个现实世界案例研究，并在框架内进行了实证评估，这标志着向可验证的自动驾驶系统信息交换迈出了一步。第一个案例展示了实时证明生成与验证，在GPU上实现了8毫秒的证明生成和1毫秒的验证；第二个案例评估了自动驾驶车辆感知栈的性能，实现了不暴露专有或机密数据的计算证明。此外，该框架可集成到AV感知栈中，以促进可验证的互操作性和隐私保护协同感知。该项目的演示代码已在GitHub上开源。