Zhigang Xu

DSRC versus 4G-LTE for Connected Vehicle Applications: A Study on Field Experiments of Vehicular Communication Performance

Dedicated short-range communication (DSRC) and 4G-LTE are two widely used candidate schemes for Connected Vehicle (CV) applications. It is thus of great necessity to compare these two most viable communication standards and clarify which one can meet the requirements of most V2X scenarios with respect to road safety, traffic efficiency, and infotainment. To the best of our knowledge, almost all the existing studies on comparing the feasibility of DRSC or LTE in V2X applications use software-based simulations, which may not represent realistic constraints. In this paper, a Connected Vehicle test-bed is established, which integrates the DSRC roadside units, 4G-LTE cellular communication stations, and vehicular on-board terminals. Three Connected Vehicle application scenarios are set as Collision Avoidance, Traffic Text Message Broadcast, and Multimedia File Download, respectively. A software tool is developed to record GPS positions/velocities of the test vehicles and record certain wireless communication performance indicators. The experiments have been carried out under different conditions. According to our results, 4G-LTE is more preferred for the nonsafety applications, such as traffic information transmission, file download, or Internet accessing, which does not necessarily require the high-speed real-time communication, while for the safety applications, such as Collision Avoidance or electronic traffic sign, DSRC outperforms the 4G-LTE.

Novel vehicle detection system based on line scan camera

In order to improve the precision and real-time character of the traditional video-based traffic detector, a novel vehicle detection system based on line scan camera is developed. In this system, the vertical view images of vehicles are captured by two line scan cameras which are mounted on the poles arm over the roadway; The distance between two cameras is 2 meters along the road. The Vehicles outline region in the captured image is extracted with a wavelet transform algorithm, and the vehicles speed is estimated by a curve correlation method. Compared with video camera, the line scan camera only detects the moving objects and the static background of the pavement in the captured image hardly changes, hence the vehicle detection algorithm has a lower complexity and consumes less time. Moreover, the measurement precision of the vehicles speed is much higher than the video-based traffic detector since the line scan camera has a frame rate of 1000Frames/s, while the rate of video camera is only 25~30 Frames/s.

Visual odometry for on-road vehicles based on trifocal tensor

The accurate positioning is the core technology of mobile robot. The paper proposes a visual odometry method based on trifocal tensor to get the high-precision positioning information of autonomous robot. Two-wheel car was used to simulate the mobile robot, where monocular camera was mounted on. We employed camera calibration algorithm to get intrinsic parameters, the IPM (Inverse Perspective Mapping) to get the top view of pavement images, the improved SURF to detect and match feature points, trifocal tensor to calculate the fundamental matrix after outliner points removing based on RANSAC algorithm and calculate the car pose from the fundamental matrix. Finally, the Kalman filter was adopted to estimate the pose of the car. Experimental results and analysis demonstrate that visual odometry based on trifocal tensor well restrain the drift error of visual positioning method.

Image Antiblurring and Statistic Filter of Feature Space Displacement: Application to Visual Odometry for Outdoor Ground Vehicle

Precise, reliable, and low-cost vehicular localization across a continuous spatiotemporal domain is an important problem in the field of outdoor ground vehicles. This paper proposes a visual odometry algorithm, where an ultrarobust and fast feature-matching scheme is combined with an effective antiblurring frame selection strategy. Our method follows the procedure of finding feature correspondences from consecutive frames and minimizing their reprojection error. The blurred image is a great challenge for localization with a sharp turn or fast movement. So we attempt to mitigate the impact of blur with an image singular value decomposition antiblurring algorithm. Moreover, a statistic filter of feature space displacement and circle matching are proposed to screen or prune potential matching features, so as to remove the outliers caused by mismatching. An evaluation of benchmark dataset KITTI and real outdoor data, with blur, low texture, and illumination change, demonstrates that the proposed ego-motion scheme significantly achieved performance with respect to the other state-of-the-art visual odometry approaches to a certain extent.

PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking

With the advent of autonomous vehicles, in particular its adaptability to harsh conditions, the research and development of autonomous vehicles attract significant attention by not only academia but also practitioners. Due to the high risk, high cost, and difficulty to test autonomous vehicles under harsh conditions, the hardware-in-the-loop (HIL) scaled platform has been proposed as it is a safe, inexpensive, and effective test method. This platform system consists of scaled autonomous vehicle, scaled roadway, monitoring center, transmission device, positioning device, and computers. This paper uses a case of the development process of tracking control for high-speed U-turn to build the tracking control function. Further, a simplified vehicle dynamics model and a trajectory tracking algorithm have been considered to build the simulation test. The experiment results demonstrate the effectiveness of the HIL scaled platform.

An Automatic Calibration System of Vehicle Motion Error under GPS Blind Area

Visual odometer or wheel speed sensor (WPS) is often used for vehicle positioning when GPS signals are obstructed for a long time in urban complex environments. Because they have always accumulated error, an automatic calibration system of vehicle motion error under GPS blind area is proposed. Firstly, when a vehicle moves to the detected area, the self-organizing network based on Zigbee sends signals to trigger the camera of road side unit (RSU), starting to collect images and preprocess. And then the signals of four different RFID tags read by vehicle mounted RFID reader are used to implement vehicle positioning. Meanwhile, vehicle position is also detected and calculated by image processing based on background difference method. Finally, two kinds of positioning information are fused according to time synchronization, which result is applied to amend accumulated positioning error produced by odometer method, to realize vehicle location automatic calibration. Experimental data shows that this method has accurate positioning information, and can effectively eliminate the vehicle accumulated error. The system has simple devices, is easy to mount and use, and can be suitable for the vehicle-road environment.