# Cybersecurity Comparison of Brain-Based Automotive Electrical and Electronic Architectures
## 8 Related Work  
Autonomous vehicles are the next step in the evolution of the automotive industry. According to the Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles J3016_202104 committee [18], there are six levels of automation for autonomous vehicles. * Level 0: No automation;
* Level 1: Driver assistance;
* Level 2: Partial automation;
* Level 3: Conditional automation;
* Level 4: High automation;
* Level 5: Full automation. The advanced electrical systems in a vehicle require constant communication within on-board components and the cloud to ensure the proper functionality and security of the vehicle. This necessity of uninterrupted communication has made the security of vehicle systems vulnerable to malicious attacks from hackers. There have been several incidents where the hackers were able to take partial control over the vehicle's autonomous functionalities. The infamous keep hack in 2015 [19] incident shock the autonomous cybersecurity community in terms of the control, safety, and security of such vehicles. In order to stay on the same page worldwide, in terms of automotive cybersecurity, the industry experts collaborated and created a standardized version for all automotive manufacturers to follow. The International Organization for Standardization (ISO) in collaboration with SAE International took the recommendations gathered in SAE/J3061, the Cybersecurity guidebook for cyber-physical vehicle systems and developed the most recent ISO/SAE 21434 [11] Standard for Road Vehicles--Cybersecurity Engineering. For existing peer-reviewed scientific work from the research community, the authors of this paper performed an in-depth search in scholarly data repositories. The following keywords were used to explore the existing work on this topic:  
Figure 20: Integration of SecOC. [17]* Cybersecurity automotive E/E architecture (CSAEEA);
* Motivation hacking E/E architecture (MHEEA);
* Autonomous vehicle SAE level (AVSAEL);
* Cybersecurity related to distributed architecture (CSRDA);
* E/E architecture for automotive security (EEAAS). Scopus, Web of Science, IEEE, Science Direct, and Google Scholar were explored for this purpose. Table 2 represents the data for this research. The authors of the paper [20] provided the security analysis of autonomous vehicle including the threat and risk analysis using STRIDE and CVSS. The authors also suggest a set of security mechanisms for the topmost threats. [PERSON] and [PERSON] explored in their work the existing safety concerns regarding autonomous vehicles [21]. As the manufacturing of autonomous vehicles is a highly inter-disciplinary domain, there is no one way of diagnosing and mitigating the threats and risks concerning the passenger and bystander safety. Their work presents a few generalized safety concerns which are worthy of attention to be resolved in a coordinated, cross-domain manner. They are open to the possibilities of having more high-risk problem scenarios that they have not covered or might emerge in the future. The main focus is to create awareness in terms of existing automotive security concerns. According to the autonomous vehicle researchers of [22], under certain conditions, the passengers could not tell the difference between a human and an AI driver. They performed a study with a level 2 autonomous vehicle. The passengers could not see the driver and they were asked to distinguish between the manual or AI driving performance. This study was mostly focused on the humanness of the autonomous functions. The authors used a Turing algorithm-based model to verify their hypothesis among their subjects. The authors of [23] explored the lane-level maneuvers to provide oversight to guarantee safety for the autonomous vehicles on the road with a survey. The modern Global Navigation Satellite Systems (GNSS) and their increased level of accuracy are facilitating the detection of road at the lane level.