Research Article

Dynamic heterogeneous redundancy-based endogenous security and safety for connected automated vehicles: Preliminary test results and assessment

¹ Purple Mountain Laboratories, Nanjing, 211111, China
² School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
³ Institute of Big Data, Fudan University, Shanghai, 200433, China

^* Corresponding author (email: wangpeng@pmlabs.com.cn)

Received: 30 December 2024
Revised: 5 June 2025
Accepted: 29 July 2025

Abstract

Safety and security are crucial for Connected and Automated Vehicles (CAVs), as they affect vehicle performance, passenger safety, and resilience to cyber threats. However, there is a gap in ensuring both safety and security, as they are often studied independently, causing existing safety mechanisms to weaken security mechanisms and vice versa. To address this gap, we propose an endogenous security and safety approach that employs dynamic heterogeneous redundancy (DHR) technologies. We first elaborate on the fundamental concept of integrated safety and security for CAVs, drawing upon the Vehicle-Road-Cloud ecosystem as well as the relevant design and development processes. Then, we design a dynamic heterogeneous redundancy-based integrated safety and security monitoring module, which is constructed around system kernel events. Empirical testing demonstrates the module maintain 100% system performance during failures and attacks through dynamic reconfiguration. Building upon this, we propose an in-depth defense solution for CAVs that combines mimicry defense, integrated safety and security monitoring, and dynamic reconfiguration. A prototype of this in-depth defense solution is implemented, followed by penetration testing to evaluate its practical effectiveness. Penetration testing across 122 vulnerability cases (including format strings and integer overflows) shows 95.9% defense success rate with 90% vulnerability coverage. Additionally, we identify the assessment of the proposed endogenous security and safety approach for CAVs, highlighting its lightweight design, robust safety and security, quantification capabilities, and tracing functions.