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The development of intelligent transportation improves road efficiency, reduces automobile energy consumption, and improves driving safety. The core of intelligent transportation is the two-way information interaction between vehicles and the road environment. At present, road environmental information can flow to the vehicle, while the vehicle’s information rarely flows to the outside world. The electronic throttle and electronic braking systems of some vehicles use sensors to get the state of the accelerator and brake pedal, which can be transmitted to the outside environment through technologies such as the Internet of Vehicles. But the Internet of Vehicles technology has not been widely used, and it relies on signal sources, which is a passive way of information acquisition. In this paper, an active identification method is proposed to get the vehicle pedal on-off state as well as the driver’s operation behavior through existing traffic facilities.

H-Bahn ("hanging railway") refers to the suspended, unmanned urban railway transportation system. Through the reasonable platform layout, H-Bahn can be easily integrated into the existing urban transit system. With the development of urban roads, the associated rail facilities can be conveniently disassembled, moved and expanded. The track beam, circuits, communication equipment, and sound insulation screen are all installed in a box-type track beam so that the system can achieve a high level of integration and intelligence. The carriage of the modern H-banh vehicle is connected with the bogies by two hanging devices. The vehicle is always running in the box-type track beam; therefore there are less possibilities of derailment. Consequently, the key work focuses on the running stability evaluation and curve negotiation performance analysis.

Mountain road winding and bumpy, traffic accidents caused by speeding frequently happened, mainly concentrated on curves. The present curve warning system research are based on Charge-coupled Device, but the existing obstacles, weather , driving at night and road conditions directly affect the accuracy and applicability. The research is of predictability to identify the curves based on the geographic information and can told the driver road information and safety speed ahead of the road according to the commercial vehicle characteristic of load, and the characteristics of the mass center to reduce the incidence of accidents. In this paper, the main research contents include: to estimate forward bend curvature through the node classification method based on the digital map.

Studies show that driving in foggy environment is a security risk, and when driving in foggy environment, the drivers are easy to accelerate unconsciously. The safety information prompted to the driver is mainly from fog lights, road warning signs and the traffic radio. In order to increase the quality of the safety tips to prevent drivers from unintended acceleration and ensure the security of driving in foggy environment, the study proposes a safety speed assessment method for driving in foggy environment, combining the information of driving environment, vehicle’s speed and the multimedia system. The method uses camera which is installed on the front windshield pillar to collect the image about the environment, and uses the dark channel prior theory to calculate the visibility. And by using the environment visibility, the safety speed can be calculated based on the kinematics theory. And it is appropriate for vehicles which have different braking performance.