The development of test method is essential for assessing autonomous vehicle safety. How to ensure real vehicle dynamic performance and create more critical scenarios is the challenge for safety test. Therefore, the novel method for autonomous vehicle safety test is necessary. The contribution of this article is twofold. First, the digital twin test method with Internet of Things (IoT) technique is purposed, which seamlessly integrates real test vehicle with virtual environment. We use Vehicle to Everything (V2X) technique to communicate the physical world and virtual environment. The real test vehicle can undergo within the virtual environment, which can create complex critical scenarios conveniently and ensure high-test confidence level. Second, we employ the fault tree analysis method to identify critical scenario element. The identified critical scenario elements can be combined to create various critical scenarios. Furthermore, the case study of autonomous emergency braking (AEB) system in pedestrian crossing collision scenario is implemented with the purposed method. A comparison of the purposed method with traditional methods shows that the purposed method has higher test confidence and more critical scenarios. This study addresses the challenge of safety test with the IoT technique and offers valuable insights for improving autonomous vehicle safety.

The Internet of Vehicles (IoV) operates in an environment characterized by asymmetric security threats, where centralized vulnerabilities create a critical imbalance that can be disproportionately exploited by attackers. This study addresses this imbalance by proposing a symmetrical security framework that integrates Blockchain and Federated Learning (FL) to restore equilibrium in the Vehicle–Road–Cloud ecosystem. The evolution toward sixth-generation (6G) technologies amplifies both the potential of vehicle-to-everything (V2X) communications and its inherent security risks. The proposed framework achieves a delicate balance between robust security and operational efficiency. By leveraging blockchain’s symmetrical and decentralized distribution of trust, the framework ensures data and model integrity. Concurrently, the privacy-preserving approach of FL balances the need for collaborative intelligence with the imperative of safeguarding sensitive vehicle data. A novel Cloud Proxy Re-Encryption Offloading (CPRE-IoV) algorithm is introduced to facilitate efficient model updates. The architecture employs a partitioned blockchain and a smart contract-driven FL pipeline to symmetrically neutralize threats from malicious nodes. Finally, extensive simulations validate the framework’s effectiveness in establishing a resilient and symmetrically secure foundation for next-generation IoV networks.

This study presents a novel multimodal heterogeneous perception cross-fusion framework for intelligent vehicles that combines data from millimeter-wave radar and camera to enhance target tracking accuracy and handle system uncertainties. The framework employs a multimodal interaction strategy to predict target motion more accurately and an improved joint probability data association method to match measurement data with targets. An adaptive root-mean-square cubature Kalman filter is used to estimate the statistical characteristics of noise under complex traffic scenarios with varying process and measurement noise. Experiments conducted on a real vehicle platform demonstrate that the proposed framework improves reliability and robustness in challenging environments. It overcomes the challenges of insufficient data fusion utilization, frequent leakage, and misjudgment of dangerous obstructions around vehicles, and inaccurate prediction of collision risks. The proposed framework has the potential to advance the state of the art in target tracking and perception for intelligent vehicles.

Hazard analysis and safety requirements are very crucial for the safety-critical system design. For the traditional vehicle system safety analysis and design, Failure Modes and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) are usually conducted to evaluate the risk. However, for Connected and Autonomous Vehicles (CAV), the autonomous system is more complex, and there are a lot of interactions among subsystems. Conventional hazard analysis methods on the basis of the linear chain of events are not applicable to CAVs because it is difficult to identify the potential hazards caused by system interactions or performance limits. A new hazard analysis method based on control theory and system theory, Systems Theory Process Analysis (STPA), can identify the potential hazards from the view of the whole system. Besides, Model-Based Systems Engineering (MBSE) approach is introduced to improve the consistency and traceability. In this article, a model-based hazard analysis method combining STPA and MBSE is proposed, which can map the development process in compliance with the International Organization for Standardization (ISO) 26262 and Safety Of The Intended Functionality (SOTIF) standards. System assumptions and architecture from system engineering foundations can be used to form the item definition in the safety analysis process. Hazards can be identified by the STPA method based on preliminary system functions. Furthermore, causal factors and safety constraints from Unsafe Control Actions (UCAs) can help developers to generate functional safety requirements and modify the preliminary system architecture. The Automatic Emergency Braking (AEB) system is taken as a typical example to demonstrate the effectiveness of this method. The hazardous state and casual factors for the AEB system is identified by STPA based on the system model, which is described by the systems modeling language (SysML). Meanwhile, the analysis results give the guidance for a safety-related system design. The proposed workflow promotes the collaboration between the system designer and safety analyzer and accelerates the system development iterations process.

Butanol is a promising alcohol fuel. Previous studies on combustion and diesel engines showed different trends in sooting tendencies of the butanol isomers (n-butanol, iso-butanol, sec-butanol and tert-butanol).The impact of butanol isomers on the particulate emissions of GDI (Gasoline Direct Injection) engines, however, has not been reported. This work examines the combustion performance and particle number emissions of a GDI engine fueled with gasoline/butanol blends in steady state modes. Each isomer was tested at blend ratios from 10% to 50% by volume. Spark timings for all the fuels are set to obtain the maximum break torque (MBT), i.e. the MBT spark timings. Results show that the particle number concentration is reduced significantly with increasing butanol content for all the isomers. Sec-Butanol/gasoline yields the lowest particle number concentration, closely followed by n-butanol/gasoline, and then iso-butanol/gasoline, tert-butanol/gasoline yielded the highest particle number concentration. The particle mean diameter decreases slightly with the addition of butanol. Similar to the trend of particle number concentration, sec-butanol/gasoline has the smallest particle sizes while tert-butanol/gasoline the largest.The difference in the particle mean diameter of the four isomers is not significant. The rapid burning angle of gasoline and butanol/gasoline blends follows the ranking of laminar flame speed, while flame development angle is affected by both the laminar flame speed and flame stretch sensitivity. The flame development angle, the rapid burning angle and the cylinder temperature profiles of n-butanol/gasoline, sec-butanol gasoline and tert-butanol/gasoline are similar at the same blend ratio.