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At present, how the application of connected vehicle technology will affect drivers’ driving psychology needs to be explored. As an important part of driving psychology, driving confidence can guide drivers to operate calmly when facing a complex traffic environment, which has an important impact on reducing accident rates and improving traffic efficiency. Based on the driving behavior data in the expressway work zone under a connected vehicle environment, this study mainly analyzed the difference between the psychological characteristics of drivers with warning information or without warning information when facing the work zone ahead. Firstly, based on driving simulation technology, the expressway work zone scene in a connected vehicle environment was designed, and the on-board human-machine interface was used to provide warning information of the work zone ahead.

According to SAE’s classification of autonomous driving, L3 systems are conditional autonomous driving. The responsibility of vehicle control and environmental monitoring are systems. But when the system requires the driver to respond its request, the driver must respond soon, and if an accident occurs, the responsibility is the driver. So, it requires the driver have ability to take over the vehicle all the time. Therefore, the length of the driver’s takeover time in different scenarios is becoming more and more important. The system needs to send a takeover signal at a certain time advance according to the driving environment, so that the driver has time to complete the takeover of the system. This article describes the takeover control flow chart of the L3 autopilot systems, and the factors that affect the takeover time, such as the time when the takeover signal is issued, the form of takeover signal, and the state of the driver.

Most of the Advanced Driver Assistance System (ADAS) applications are aiming at improving both driving safety and comfort. Understanding human drivers' driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system performance, in particular, the acceptance and adaption of ADAS to human drivers. The research presented in this paper focuses on the classification and identification for personalized driving styles. To motivate and reflect the information of different driving styles at the most extent, two sets, which consist of six kinds of stimuli with stochastic disturbance for the leading vehicles are created on a real-time Driver-In-the-Loop Intelligent Simulation Platform (DILISP) with PanoSim-RT®, dSPACE® and DEWETRON® and field test with both RT3000 family and RT-Range respectively.

A newly developed process allows the near-net shape fabrication of alumina/aluminum composite bodies via the immersion of a sacrificial oxide preform into a molten aluminum alloy bath. The resulting composite possesses an attractive range of properties for application in several automotive components. These properties include: high strength and stiffness, appreciable thermal and electrical conductivity, high strength at elevated temperatures, coefficient of thermal expansion of 10 X 10-6 C-1 and relative ease of machinability. Low cost fabrication renders this material/process ideal for components such as brake rotors and calipers, cylinder bore liners, piston components.