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Technical Paper

Camera-Radar Data Fusion for Target Detection via Kalman Filter and Bayesian Estimation

Target detection is essential to the advanced driving assistance system (ADAS) and automatic driving. And the data fusion of millimeter wave radar and camera could provide more accurate and complete information of targets and enhance the environmental perception performance. In this paper, a method of vehicle and pedestrian detection based on the data fusion of millimeter wave radar and camera is proposed to improve the target distance estimation accuracy. The first step is the targets data acquisition. A deep learning model called Single Shot MultiBox Detector (SSD) is utilized for targets detection in consecutive video frames captured by camera and further optimized for high real-time performance and accuracy. Secondly, the coordinate system of camera and radar are unified by coordinate transformation matrix. Then, the parallel Kalman filter is used to track the targets detected by radar and camera respectively.
Technical Paper

A Localization System for Autonomous Driving: Global and Local Location Matching Based on Mono-SLAM

The utilization of the SLAM (Simultaneous Localization and Mapping) technique was extended from the robotics to the autonomous vehicles for achieving the positioning. However, SLAM cannot obtain the global position of the vehicle but a relative one to the start. For sake of this, a fast and accurate system was proposed to obtain both the local position and the global position of vehicles based on mono-SLAM which realized the SLAM by using monocular camera with a lower cost and power consumption. Firstly, the rough latitude and longitude of current position was obtained by using common GPS without differential signal. Then, the Mono-SLAM operated on the consecutive video frames to generate the localization and local trajectory and its accuracy was further improved by utilizing the IMU information. After that, a piece of Map centered in the rough position obtained by common GPS was downloaded from the Open Street Map.
Technical Paper

A New Method of Target Detection Based on Autonomous Radar and Camera Data Fusion

Vehicle and pedestrian detection technology is the most important part of advanced driving assistance system (ADAS) and automatic driving. The fusion of millimeter wave radar and camera is an important trend to enhance the environmental perception performance. In this paper, we propose a method of vehicle and pedestrian detection based on millimeter wave radar and camera. Moreover, the proposed method complete the detection of vehicle and pedestrian based on dynamic region generated by the radar data and sliding window. First, the radar target information is mapped to the image by means of coordinate transformation. Then by analyzing the scene, we obtain the sliding windows. Next, the sliding windows are detected by HOG features and SVM classifier in a rough detect. Then using the match function to confirm the target. Finally detecting the windows in a precision detection and merging the detecting windows. The target detection process is carried out in the following three steps.
Technical Paper

A Test Method and Simulation Study of PMMA Glazing on Motion Deviation

For achieving vehicle light weighting, the motion deviation is calculated for substitution of PMMA glazing for inorganic glass. In this paper, a test method is proposed to measure and calculate the motion deviation of the dual-curvature glass. To simulate the dual-curvature glass, the torus surface is fitted with least square method according to the window frame data, which are measured by Coordinate Measuring Machine. By using this method, the motion deviation of PMMA glazing and inorganic glass can be calculated, which can not only validate the effectiveness of motion simulation, but also compare the performances. The results demonstrate that the performance of PMMA glazing is better than that of inorganic glass and the simulation results is validated.
Technical Paper

A New Method for Determination of Forming Limit Diagram Based on Digital Image Correlation

In this paper, a new method for determining the forming limit diagram (FLD) of thin sheet metals, called DIC-Grid method, is proposed based on digital image correlation (DIC) technique. It's assumed that there exists one virtual grid with an initial diameter of 2.5mm, which is usually the same dimension as the grid in traditional circular grid analysis, close to the crack of specimen, and the limit strain point on FLD is determined by the deformation of this virtual grid. The DIC-Grid method has been compared with traditional circular grid analysis and the standard ISO/FDIS 12004-2 in Nakajima tests. The results show that the forming limit strains obtained by the newly proposed method are more stable and precise. Furthermore, DIC-Grid method can avoid the measurement error which exists in the circular grid analysis. Meanwhile, it overcomes the shortcomings of time-consuming data processing and non-applicable for unrealistic strain distribution in the method of ISO standard.