While this transformation enhances the driving experience and vehicle performance, it also exposes automotive electronic systems to numerous safety risks. Ensuring the safety of automotive electronic systems has become a key issue for the development of the automotive industry.
From the perspective of automotive electronic system architecture, modern vehicles contain a large number of electronic control units (ECUs), interconnected through various in-vehicle network protocols such as CAN, LIN, and Ethernet, forming a complex network topology. Simultaneously, vehicles utilize technologies like 5G/V2X, Bluetooth, and Wi-Fi to achieve real-time communication with the external environment, including interaction with cloud servers, other vehicles, and road infrastructure. While this high degree of networking and intelligence brings numerous convenient functions such as autonomous driving assistance, remote diagnostics and control, and intelligent connectivity services, it also significantly expands the attack surface of automotive electronic systems.
Functional safety is a crucial aspect of safety considerations in next-generation automotive electronic design. It primarily focuses on how to prevent dangerous events from occurring when electronic and electrical systems experience random hardware failures or systemic malfunctions. For example, in a brake control system, if the brake control module malfunctions and safety measures are not taken in time, the vehicle may fail to brake properly, potentially leading to a serious traffic accident. In automotive electronic design, to meet functional safety requirements, development is often based on the ISO 26262 standard. This standard uses hazard analysis and risk assessment to identify safety objectives and allocate safety requirements to the system, hardware, and software levels. For instance, in an electronic power steering (EPS) system, when the control unit detects an internal fault such as an abnormal torque sensor, it immediately activates safety mechanisms, such as displaying a red warning light on the instrument panel and gradually increasing steering effort to allow the driver to take over; if the driver does not respond, the system will trigger emergency lane keeping and bring the vehicle to a safe stop. Furthermore, systems like Bosch's ESP® control system employ a dual-core lockstep architecture, where two processors simultaneously execute the same instructions and compare the results. If an inconsistency is detected, the system can switch to a safe state within milliseconds to meet the highest level of safety requirement, ASIL D.
Cybersecurity is also an indispensable aspect of next-generation automotive electronics design. Modern automobiles face multi-layered attack threats from external interfaces, in-vehicle networks, and backend services. From an external interface perspective, T-Box, Bluetooth, and tire pressure monitoring systems can all become entry points for hackers; the CAN bus and Ethernet within the in-vehicle network are not invincible; and backend services such as OTA updates and remote diagnostics, if poorly secured, can also provide opportunities for hackers. For example, a vulnerability test conducted by a car manufacturer in 2024 showed that the success rate of infiltrating the vehicle network through malicious charging stations was as high as 31%, allowing hackers to tamper with battery management system parameters, leading to serious consequences such as thermal runaway. To address these cybersecurity threats, the automotive industry has developed a series of innovative protection technologies. For instance, automotive AI firewalls employ lightweight cryptography and can process over 1000 in-vehicle data entries per second, enabling real-time anomaly detection. It achieves high-performance encryption on resource-constrained ECUs by optimizing symmetric encryption algorithms, reducing energy consumption by 30-40% compared to traditional algorithms. Utilizing an abnormal traffic identification model based on LSTM networks, it establishes a normal communication baseline and can detect abnormal messages at the 0.01-second level. Simultaneously, it provides key storage and encryption operation protection for critical ECUs through a Hardware Security Module (HSM). Furthermore, intrusion detection systems (IDS) are constantly innovating. For example, CAN bus fingerprinting technology uses ECU clock offset characteristics to identify spoofed nodes with an accuracy exceeding 99.2%; load semantic analysis prevents attacks by detecting whether braking command values exceed physically possible ranges (e.g., a sudden change from 0.5g to 1.2g).
Data security is also crucial in next-generation automotive electronics design. Cars collect vast amounts of data during operation, including driver information, driving trajectories, and vehicle status data. The leakage of this data not only infringes on user privacy but can also be exploited by hackers, threatening vehicle security. For example, hackers who obtain vehicle driving trajectory data could analyze the owner's travel habits and choose an opportune moment to launch an attack. Therefore, automotive electronics design requires measures such as data encryption, access control, and data backup and recovery to ensure data security. During data transmission, encryption technology ensures the confidentiality and integrity of data; during data storage, sensitive data is encrypted and strictly accessed, allowing only authorized systems and users to access it. Furthermore, important data should be backed up regularly, and a comprehensive data recovery plan should be developed to address data loss or corruption.
Beyond the aforementioned safety issues, anticipated functional safety is another area of focus for next-generation automotive electronics design. It primarily addresses hazards arising from performance limitations under fault-free conditions, such as sensor misidentification (e.g., mistaking a truck's white cargo box for the sky), algorithm failure in extreme weather (missing pedestrians in dense fog), and human-machine interface design flaws (not providing sufficient time for driver takeover when the autonomous driving system disengages). To address these issues, the industry has adopted a series of technical measures. For example, Tesla uses Generative Adversarial Networks (GANs) to simulate obstacles in rain and fog, improving the camera's recognition capabilities in low-visibility environments; Waymo's fifth-generation system fuses LiDAR, camera, and millimeter-wave radar data at the feature level rather than the decision level, significantly reducing the risk of single-sensor failure. In terms of development processes, the ISO/PAS 21448 standard is followed, employing four steps—scene recognition, functional deficiency trigger condition analysis, risk control strategy design, and verification and validation—to ensure anticipated functional safety.
The safety considerations for next-generation automotive electronic design encompass multiple aspects, including functional safety, cybersecurity, data security, and anticipated functional safety. The automotive industry needs to address this from multiple levels, including system architecture design, technology development, standards setting, and safety management, comprehensively utilizing various safety technologies and measures to build a comprehensive, multi-layered automotive electronic safety protection system. Only in this way can the advantages of intelligent and connected vehicles be fully leveraged while effectively ensuring the safety of vehicles and occupants, thus promoting the healthy and sustainable development of the automotive industry.
