露天矿中基于谷歌地球的车辆三维助驾系统

针对露天矿恶劣驾驶环境、矿车司机盲区等因素引发的事故,运用全球定位系统(GPS)、无线远程数据广播模块和谷歌地球(GE)三维图形控制引擎技术,开发露天矿车辆安全三维助驾系统,分别为露天矿司机和远程调度人员提供三维参考信息与实时车辆监测信息。该系统通过定制露天矿三维数字地图,监测车辆实时位置的三维车辆模型或标记点,显示安全驾驶引导路线,可以提高露天矿车辆驾驶的安全性。

GPS and Google Earth based 3D assisted driving system for trucks in surface mines

In order to reduce the number of surface mining accidents related to low visibility conditions and blind spots of trucks and to provide 3D information for truck drivers and real time monitored truck information for the remote dispatcher, a 3D assisted driving system (3D-ADS) based on the GPS, mesh-wireless networks and the Google-Earth engine as the graphic interface and mine-mapping server, was developed at Virginia Tech. The research results indicate that this 3D-ADS system has the potential to increase reliability and reduce uncertainty in open pit mining operations by customizing the local 3D digital mining map, con-structing 3D truck models, tracking vehicles in real time using a 3D interface and indicating available escape routes for driver safety.

Zigbee/Google Earth based assisted driving system in mining

The Assisted Driving System (ADS) for haul trucks operating in surface mining and construction sites is to reduce accidents related to low visibility conditions. This system is based on the GPS, Zigbee, and the Google-Earth engine as the graphic interface and mine-mapping server. The system has the capability to pin-point and track vehicles in real time using a 3D interface, which is based on user-based AutoCAD mine maps using the Google-Earth graphics interface. All equipped vehicles are shown in a 3D mine map stored in a local server through a wireless network. When low visibility conditions are present, the system indicates available exit/escape routes for driver safety. The ADS potentially increases reliability and reduces uncertainty in open pit mining operations.

Road Geometry Recovery Using On-board Camera in Various Conditions

A 3D road geometry reconstruction system using on-board camera for vehicle driver assistance is developed. Given calibration of the camera, 3D structure of the road in front of the vehicle is built via multiple vanishing points estimation and local parallelism of the road. The road will be modeled as a piecewise linear approximation of space curve. Vanishing points are estimated through line grouping and histogram method in the image. The system is not restricted to the roads which lie on 2D plane, but also is capable of recovering a full 3D geometry. The ambiguity of distance （scale） is resolved by usage of parallel line segnents an the road image.

Characterization of Driving Style and the Influence of Distraction Based on Non-intrusive Driving Parameters

It is difficult to model human behavior because of the variability in driving styles and driving skills. However, for some driver assistance systems, it is necessary to have knowledge of that behavior to discriminate potentially hazardous situations, such as distraction, fatigue or drowsiness. Many of the systems that look for driver distraction or drowsiness are based on intrusive means （analysis of the electroencephalogram--EEG） or highly sensitive to operating conditions and expensive equipment （eye movements analysis through artificial vision）. A solution that seeks to avoid the above drawbacks is the use of driving parameters This article presents the conclusions obtained after a set of driving simulator tests with professional drivers with two main objectives using driving variables such as speed profile, steering wheel angle, transversal position on the lane, safety distance, etc., that are available in a non-intrusive way： （1） To analyze the differences between the driving patterns of individual drivers; and （2） To analyze the effect of distraction and drowsiness on these parameters. Different scenarios have been designed, including sequences with distractions and situations that cause fatigue. The analysis of the results is carried out in time and frequency domains in order to identify situations of loss of attention and to study whether the evolution of the analyzed variables along the time could be considered independent of the driver.

Path planning and stability control of collision avoidance system based on active front steering

Vehicle collision avoidance system is a kind of auxiliary driving system based on vehicle active safety,which can assist the driver to take the initiative to avoid obstacles under certain conditions,so as to effectively improve the driving safety of vehicle.This paper presents a collision avoidance system for an autonomous vehicle based on an active front steering,which mainly consists of a path planner and a robust tracking controller.A path planner is designed based on polynomial parameterization optimized by simulated annealing algorithm,which plans an evasive trajectory to bypass the obstacle and avoid crashes.The dynamic models of the AFS system,vehicle as well as the driver model are established,and based on these,a robust tracking controller is proposed,which controls the system to resist external disturbances and work in accordance with the planning trajectory.The proposed collision avoidance system is testified through CarSim and Simulink combined simulation platform.The simulation results show that it can effectively track the planning trajectory,and improve the steering stability and anti-interference performance of the vehicle.