The caveat to these additional capabilities is issues like cybersecurity, complexity, etc. This paper is an exploration into FuSa and CAVs and will present a systematic approach to understand challenges and propose potential framework, Intelligent Vehicle Monitoring for Safety and Security (IVMSS) to handle faults/malfunctions in CAVs, and specifically autonomous systems.
Cyber assurance of heavy trucks is a major concern with new designs as well as with supporting legacy systems. Many cyber security experts and analysts are used to working with traditional information technology (IT) networks and are familiar with a set of technologies that may not be directly useful in the commercial vehicle sector. To help connect security researchers to heavy trucks, a remotely accessible testbed has been prototyped for experimentation with security methodologies and techniques to evaluate and improve on existing technologies, as well as developing domain-specific technologies. The testbed relies on embedded Linux-based node controllers that can simulate the sensor inputs to various heavy vehicle electronic control units (ECUs). The node controller also monitors and affects the flow of network information between the ECUs and the vehicle communications backbone.
Automotive safety is always the focus of consumers, the selling point of products, the focus of technology. In order to achieve automatic driving, interconnection with the outside world, human-automatic system interaction, the security connotation of intelligent and connected vehicles (ICV) changes: information security is the basis of its security. Functional safety ensures that the system is operating properly. Behavioral safety guarantees a secure interaction between people and vehicles. Passive security should not be weakened, but should be strengthened based on new constraints. In terms of information safety, the threshold for attacking cloud, pipe, and vehicle information should be raised to ensure that ICV system does not fail due to malicious attacks. The cloud is divided into three cloud platforms according to functions: ICVs private cloud, TSP cloud, public cloud.
This SAE EDGE™ Research Report identifies key unsettled issues of interest to the automotive industry regarding the new generation of sensors designed for vehicles capable of automated driving. Four main issues are outlined that merit immediate interest: First, specifying a standardized terminology and taxonomy to be used for discussing the sensors required by automated vehicles. Second, generating standardized tests and procedures for verifying, simulating, and calibrating automated driving sensors. Third, creating a standardized set of tools and methods to ensure the security, robustness, and integrity of data collected by such sensors. The fourth issue, regarding the ownership and privacy of data collected by automated vehicle sensors, is considered only briefly here since its scope far exceeds the technical issues that are the primary focus of the present report. SAE EDGE™ Research Reports are preliminary investigations of new technologies.
In the near future, vehicles will operate autonomously and communicate with their environment. This communication includes Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) communication, and comunication with cloud-based servers (V2C). To improve the resilience of remote diagnostic communication between a vehicle and external test equipment against cyberattacks, it is imperative to understand and analyze the functionality and vulnerability of each communication system component, including the wired and wireless communication channels. This paper serves as a continuation of the SAE Journal publication on measures to prevent unauthorized access to the in-vehicle E/E system , explains the components of a cyber-physical system (CPS) for remote diagnostic communication, analyzes their vulnerability against cyberattacks and explains measures to improve the resiliance.
Combatting the modification of automotive control systems is a current and future challenge for OEMs and suppliers. ‘Chip-tuning’ is a manifestation of manipulation of a vehicle's original setup and calibration. With the increase in automotive functions implemented in software and corresponding business models, chip tuning will become a major concern. Recognizing and reporting of tuned control units in a vehicle is required for technical as well as legal reasons. This work approaches the problem by capturing the behavior of relevant control units within a machine learning system called a recognition module. The recognition module continuously monitors vehicle's sensor data. It comprises a set of classifiers that have been trained on the intended behavior of a control unit before the vehicle is delivered. When the vehicle is on the road, the recognition module uses the classifier together with current data to ascertain that the behavior of the vehicle is as intended.