Desarrollo de un Manejador de Dispositivos Utilizando el Modelo Windows Driver Model (wdm) para Windows 98 y 2000

Polibits ◽  
2001 ◽  
Vol 24 ◽  
pp. 26-34
Author(s):  
Rubén Peredo Valderrama ◽  
Rubén Aquino
2010 ◽  
Vol 428-429 ◽  
pp. 440-443
Author(s):  
Xiao Guang Fu ◽  
Xiao Jun Wang

Universal serial bus (USB) is a kind of new bus interface specification, whose characteristics are convenience, high speed, easiness to expand, low cost and low disturbance, etc. So it is extremely fit to be the communication interface between host computer and peripherals. This paper is mainly about the development of data collecting system based on of USB, including hardware design, firmware design, equipment driver programming and application software design based on Windows driver model (WDM). This system works stable. Both the sampling precision and the data transmission speed have reached expected effect.


Author(s):  
Ju Xie ◽  
Xing Xu ◽  
Feng Wang ◽  
Haobin Jiang

The driver model is the decision-making and control center of intelligent vehicle. In order to improve the adaptability of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the skilled driver under the driver-vehicle-road closed-loop system, a kind of human-like longitudinal driver model for intelligent vehicles based on reinforcement learning is proposed. This paper builds the lateral driver model for intelligent vehicles based on optimal preview control theory. Then, the control correction link of longitudinal driver model is established to calculate the throttle opening or brake pedal travel for the desired longitudinal acceleration. Moreover, the reinforcement learning agents for longitudinal driver model is parallel trained by comprehensive evaluation index and skilled driver data. Lastly, training performance and scenarios verification between the simulation experiment and the real car test are performed to verify the effectiveness of the reinforcement learning based longitudinal driver model. The results show that the proposed human-like longitudinal driver model based on reinforcement learning can help intelligent vehicles effectively imitate the speed control behavior of the skilled driver in various path-following scenarios.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shan Fang ◽  
Lan Yang ◽  
Tianqi Wang ◽  
Shoucai Jing

Traffic lights force vehicles to stop frequently at signalized intersections, which leads to excessive fuel consumption, higher emissions, and travel delays. To address these issues, this study develops a trajectory planning method for mixed vehicles at signalized intersections. First, we use the intelligent driver car-following model to analyze the string stability of traffic flow upstream of the intersection. Second, we propose a mixed-vehicle trajectory planning method based on a trigonometric model that considers prefixed traffic signals. The proposed method employs the proportional-integral-derivative (PID) model controller to simulate the trajectory when connected vehicles (equipped with internet access) follow the optimal advisory speed. Essentially, only connected vehicle trajectories need to be controlled because normal vehicles simply follow the connected vehicles according to the Intelligent Driver Model (IDM). The IDM model aims to minimize traffic oscillation and ensure that all vehicles pass the signalized intersection without stopping. The results of a MATLAB simulation indicate that the proposed method can reduce fuel consumption and NOx, HC, CO2, and CO concentrations by 17%, 22.8%, 17.8%, 17%, and 16.9% respectively when the connected vehicle market penetration is 50 percent.


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