VehicletoEverything (V2X) communication is expected to accomplish a longstanding goal of the Connected and Autonomous Vehicle (CAV) community to bring connected vehicles to roads on a large scale. A major challenge, and perhaps the biggest hurdle on the path towards this goal, is the scalability issues associated with it, especially when vehicular safety is concerned. As a major stakeholder, Cellular V2X (CV2X) community, which is based on the 3rd Generation Partnership Project (3GPP), has long been trying to research on whether vehicular networks are able to support the safetycritical applications in highdensity vehicular scenarios. This paper attempts to answer this question by first presenting an overview on the scalability challenges faced by 3GPP Release 14 Long Term Evolution CV2X (LTEV2X) using the PC5 sidelink interface for low and heavydensity traffic scenarios. Next, it demonstrates a series of solutions that address network congestion, packet losses, and other scalability issues associated with LTEV2X to enable this communication technology for commercial deployment. In addition, a brief survey is provided into 3GPP Release 16 5G New Radio V2X (NRV2X) that utilizes the NR sidelink interface and works as an evolution of CV2X towards better performance for V2X communications, including new enhanced V2X (eV2X) scenarios that possess ultralowlatency and highreliability requirements.

With the spread adoption of artificial intelligence, the great challenges confronted by the intelligent safety concernsafety of the intended functionality has become the biggest roadblock to the mass production of highlevel automated vehicles, notably arising from perception algorithm deficiencies. This paper focuses a cutin scenario, dividing this scenario into lowrisk and highrisk segments predicated on the kinetic energy field, and the mental activities of passengers on prefrontal cortex, are analyzed within these delineated segments. Two experiments are then conducted, leveraging driving simulators and realworld vehicles, respectively. Experiment results indicate that high risk may result in the passengers' mental activity on prefrontal cortex change. This revelation posits a potential avenue for augmenting the intended functionality of automated vehicle by using passengers' physiological state.