The rapid development of the Intelligent connected vehicle (ICV) industry has stimulated technological innovation in energy and communication while also highlighting the need for effective policies and road safety measures. Understanding and addressing road safety issues in the context of ICVs can contribute to ICV development and safe driving. This paper employs a knowledge mapping approach to scientifically and intuitively demonstrate research on the road safety issues of ICV over the last decade. By utilizing bibliometric tools such as CiteSpace, VOSviewer, and SciMAT, a total of 3661 original articles from the Web of Science are examined to explore three aspects. Firstly, the study investigates the collaborative relationships among authors and institutions within the industry. Secondly, it summarizes major research topics by analyzing and clustering keywords. Lastly, the paper identifies research hotspots and predicts future research directions. The findings reveal a dynamic field characterized by close collaboration among diverse institutions, with China and the United States emerging as the most active countries and mathematics and computer science journals becoming mainstream. According to three bibliometric tools, the research topics primarily revolve around three areas: Vehicular ad hoc Networks (VANET), intelligent transportation systems (ITS), and network security. Machine learning and V2X communication are predicted to be essential research topics in the next stage. Research on traffic accidents still has potential as the number of ICVs increases.

Crashes involving vulnerable road users (VRUs) are types of traffic accidents which take up a large proportion and cause lots of casualties. With methods of statistics and accident reconstruction, this research investigates 378 actual traffic collisions between vehicles and VRUs in China in 2021 to obtain human, vehicle, and road factors that affect the injury severity. The paper focuses on risky behaviors of VRUs and typical scenarios such as non-use of the crosswalk, violation of traffic lights, stepping into the motorway, and riding against traffic. Then, based on the Bayesian General Ordinal Logit model, influencing factors of injury severity in 168 VRU accidents are analyzed. Results demonstrate that the probability of death in an accident will rise when the motorist is middle-aged and the VRU is an e-bicycle rider; the probability of death in an accident will greatly decrease when the VRU bears minor responsibility. Therefore, middle-aged motorists and e-bicycle riders should strengthen safety consciousness and compliance with regulations to prevent accident and reduce injury for VRUs. In addition, helmet-wearing will help to reduce riders’ injuries. This research may provide ideas for intelligent vehicles to avoid collisions with risky VRUs.

Traffic accident reconstruction is essential for reducing accidents and traffic safety prevention. At present, data from event data recorder (EDR), global positioning system (GPS), and on-site video recordings have become important evidence for traffic accident reconstruction. This paper first reviews and analyzes the mainstream theories and methods of existing traffic accident reconstruction, clarifying their technical characteristics and practical application problems. On this basis, a computer simulation-based traffic accident reconstruction method is proposed, which integrates and aligns vehicle-related EDR data, GPS data, and on-site video parameters, and the method is applied to the reconstruction analysis of actual traffic accident cases. The real case verification results show that under the proposed multisource data alignment and fusion framework, the vehicle speed reconstruction error is less than 1.0 km/h, the final parking position error is only 0.3 m, and the accuracy of reproducing the accident process exceeds 95%. EDR, GPS, and on-site video data provide reliable parameter support for accident reconstruction models, which effectively improves the reliability, authenticity, and analytical efficiency of computer simulation-based reconstruction. Furthermore, in complicated situations where some electronic records are invalid and the on-site evidence is abundant, the computer simulation technique can combine the available accident information to precisely analyze and identify the accident process, showing its importance. Moreover, the suggested approach can establish a virtual–real integrated and credible accident scenario, offering a dependable basis for examining the safety of intelligent vehicles.

In the original version of the book, the following correction has been made: The title of Chapter 44 has been changed from “Prediction Model of Accident Vehicle Speed Based on Artificial Intelligence Decision Free Algorithm” to “Prediction Model of...