With the rapid advancement of intelligent and connected vehicles, in-vehicle networks face increasing demands for high-capacity data processing and transmission. Traditional static scheduling methods in Time Sensitive Network (TSN) can no longer satisfy the stringent real-time requirements of dynamic environments. The scheduling of time-sensitive traffic is typically addressed through offline computation, producing a mathematically constrained schedule that is subsequently deployed to end nodes or switches for configuration. However, such static deployment fails to accommodate dynamic scenarios. Consequently, this study proposes a dynamic incremental scheduling framework for TSN based on Deep Reinforcement Learning (DRL). The proposed framework integrates static global optimization with rapid dynamic adaptability. In the static phase, a Deep Q-Network (DQN) learns the priority ordering of time-sensitive flow scheduling under the given network state, thereby guiding a heuristic scheduler to efficiently allocate link resources. In the dynamic phase, an incremental scheduling strategy locally reconfigures resources for newly introduced flows without affecting existing schedules, thereby enhancing responsiveness and feasibility. Furthermore, this study proposes a load-balancing-guided routing set generation method that jointly optimizes path hop count and link load, enabling coordinated scheduling and routing. Comparative analysis under various network load scenarios demonstrates that the proposed method significantly reduces the response time of dynamic flow scheduling by 35% compared to the Satisfiability Modulo Theories (SMT) algorithm, while maintaining scheduling feasibility.

The development of intelligent networked vehicles has put forward the requirements of high bandwidth, high real-time and high reliability for automotive network communication. Time sensitive network has become an important research content of vehicle Ethernet because of its high-precision synchronization, deterministic delay, redundant communication and other characteristics. This paper proposes an instruction delay time model in vehicle-mounted scenarios, and based on OMNeT++ simulation platform and improved CoRE4INET framework, combined with vehicle-mounted network traffic scenarios, designs network topology and simulation traffic, simulates and analyzes the performance of Cyclic Queuing and Forwarding (CQF) mechanism, and compares it with Time Aware Shaper (TAS) mechanism. The simulation results show that compared with Time Aware Shaper mechanism, the Cyclic Queuing and Forwarding mechanism has better performance for aperiodic messages in the vehicle-based scenario designed in this paper, and can effectively reduce the end-to-end delay of messages.

Time-sensitive networking (TSN) will be widely used in automotive industry and industrial automation because it can provide deterministic transmission. Most of the TSN traffic shaping mechanisms rely on clock synchronization between different devices in the network. However, time-delay attacks (TDAs) can interfere with synchronization, which further reduces traffic transmission quality. Although some studies have proposed detection strategies against TDAs, they are oriented to traditional ethernet and restricted by network architecture and devices. Therefore, this paper first analyzes the impact of TDAs on traffic transmission quality under different combinations of Time-Aware Shaper (TAS) or Cyclic Queue Forwarding (CQF) with link redundancy. Then, this paper provides a distributed monitoring parameter using Per-Stream Filtering and Policing (PSFP) for the detection and localization of TDAs, in which a modified token bucket mechanism is applied. Finally, the paper models the application of TSN in automotive industry for verification and further evaluates the effectiveness of this parameter through simulation. The results show that TDAs can cause undesirable variations in traffic latency under TAS and CQF. While proper parameter configuration and link redundancy can mitigate the impact of TDAs, they cannot prevent them. Besides, the proposed parameter helps detect and locate TDAs in TSN effectively.

With the rapid development of connected vehicles, people can get a better driving experience. However, the interconnection with the external network may bring growing accidents caused by cybersecurity vulnerabilities. As a result, automakers are paying more attention to cybersecurity and spending more cost on developing cybersecurity defense mechanisms. Threat analysis and risk assessment (TARA) is an efficient method to ensure the defense effect and greatly save costs in the early stage of vehicle development. It analyzes the threat of vehicle systems and determines the hierarchical defense and corresponding mitigations according to the potential threat to the system. This paper gives an overview of threat analysis and risk assessment in the automotive field. First, a novel classification of different TARA methods has been proposed. The existing methods have been analyzed and compared. Then, we have found some commonly used tools applied to TARA and compared their performance. After that, a concept named attack-defense mapping is proposed to figure out how to map the already found threats and vulnerabilities of the system to the appropriate mitigations. At last, the future development directions of TARA in the automotive domain have been discussed.

With the development of intelligent and connected vehicles, onboard Ethernet will play an important role in the next generation of vehicle network architectures. It is well established that accurate timing and guaranteed data delivery are critical in the automotive environment. The time-sensitive network (TSN) protocol can precisely guarantee the time certainty of the key signals of automotive Ethernet. With the time-sensitive network based on automotive Ethernet being standardized by the TSN working group, the TSN has already entered the vision of the automotive network. However, the security mechanism of the TSN protocol is rarely discussed. First, the security of the TSN automotive Ethernet as a backbone E/E (electrical/electronic) architecture is analyzed in this paper through the Microsoft STRIDE threat model, and possible countermeasures for the security of automotive TSNs are listed, including the security protocol defined in the TSN, so that the TSN security protocol and the traditional protection technology can form a complete automotive Ethernet protection system. Then, the security mechanism per-stream filtering and policing (PSFP) defined in IEEE 802.1Qci is analyzed in detail, and an anomaly detection system based on PSFP is proposed in this paper. Finally, OMNeT++ is used to simulate a real TSN topology to evaluate the performance of the proposed anomaly detection system (ADS). As a result, the protection strategy based on 802.1Qci not only ensures the real-time performance of the TSN but can also isolate individuals with abnormal behavior and block DoS (denial of service) attacks, thus attaining the security protection of the TSN vehicle-based network.

With the increasing connectivity of modern vehicles, protecting systems from attacks on cyber is becoming crucial and urgent. Meanwhile, a vehicle should guarantee a safe and comfortable trip for users. Therefore, how to design a cybersecurity-critical system in vehicles with safety and user experience (UX) considerations is increasingly essential. However, most co-design methods focus on safety engineering with attack concerns and do not discuss conflicts and integration, and few contain the UX aspect. Besides, most existing approaches are abstract at a high level without practical guidelines. This paper presents a literature review of existing safety and security design approaches and proposes a systematic approach for cybersecurity design of in-vehicle network systems based on the guideline in SAE J3061. The trade-off analysis is performed by using association keys and the proposed affecting map. The design process of an example Diagnostic on Internet Protocol (DoIP) system is reported to show how the approach works. Compared with the existing approaches, the proposed one considers safety, cybersecurity, and UX simultaneously, solves conflicts qualitatively or quantitatively, and obtains trade-off design requirements. This approach is applicable to the cybersecurity-driven design of in-vehicle network systems in the early stage with safety and UX considerations.