The Internet of Things plays an indispensable role in the development of connected vehicles, which will pave the way for road safety applications. In recent years, the concept of a cooperative collision warning system (CCWS) has been introduced and developed to enhance road safety, and it has been seen as a typical Internet-of-Vehicles application. In most CCWSs, it is vital to have a detection mechanism based on trajectory predictions where the uncertainties associated with vehicular state and motion are complex. However, most available approaches in this regard did not consider these uncertainties. Hence, this article proposes a new collision risk assessment (CRA) method where sigma trajectories that include multiple possible trajectories considering multiple aspects of vehicular motion are designed to cope with vehicular uncertainties. Our method is implemented in a novel server-based architecture, which is different from the commonly used vehicle-based controlled CCWSs. The CRA is provided as a service by a cloud server. The proposed method and architecture are validated and evaluated through extensive real-world experiments. Experimental results show that our method outperforms a referenced method in terms of CRA and achieves better robustness in tolerating communication delays and dropouts. Latencies in CRA service were analyzed, and it was found that powerful computing resources provided by cloud servers can significantly decrease computational cost, which will indirectly compensate for communication costs in the future. Based on our high-performance CRA method, the proposed architecture can be regarded as a novel option for CCWS design.

Autonomous driving, as an emerging technology, is developing rapidly. However, a number of obstacles appear to be holding back the widespread adoption of autonomous driving. Cybersecurity issues, which have a significant impact both technically and psychologically on people's willingness to use autonomous driving, are one of the most significant barriers. Attackers have adopted increasingly diverse threat methods, increasing cyber-attacks. Despite these threats, global standards have been developed for road vehicle safety and cybersecurity. These standards are intended to guide manufacturers of automotive systems. For instance, the ISO and SAE have jointly released the international standard ISO/SAE 21434 (Road Vehicles-Cybersecurity Engineering) for road vehicle cybersecurity. However, focusing on the development of onboard systems alone when examining the application of these standards is not sufficient. Currently, we lack a methodology to directly apply the ISO/SAE 21434 standard to automotive system engineering projects. In particular, the overall framework of cybersecurity design for intelligent connected vehicle systems needs to be developed. This paper proposes an overall cybersecurity assurance framework (ODFa ² ) based on the ISO/SAE 21434 standard to assist system engineers in understanding security risks, evaluating the feasibility of attack implementation, and determining the trade-off methods for security countermeasures. We illustrate through a case study how this model-based framework may be applied to cybersecurity engineering in intelligent connected vehicles and mobility service platforms. Furthermore, the results of comparative experiments indicate that the proposed strategy optimization algorithm outperforms existing algorithms in terms of time efficiency under certain conditions.

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs’ localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios.

The constraints imposed by traffic regulations and road geometries lead to a certain degree of geo-spatial similarity in vehicular behaviors, a factor overlooked in existing studies on vehicle motion prediction. To leverage the similarity, this paper introduces three novel vehicle motion prediction algorithms driven by geo-spatial and temporal relations within a classical Kalman filter (KF) framework, integrating Tobler's first law of geography and Newton's second law of motion. These algorithms utilize three specialized geo-spatial and temporal search mechanisms to extract geo-spatial dependencies from a spatial trajectory database and concurrently propel the KF prediction processes. Particularly, spatial interpolation and Dempster-Shafer reasoning modules are introduced to boost the algorithms' performance. We validate these methods using real driving data in public environments, demonstrating their state-of-the-art performance; and further examine their performance evolution in space domain and investigate their robustness in missing-measurement situations, which are unexplored in existing literature.