Lab of Intelligent and Connected Vehicles

Fusion Perception - Multisource Information Fusion

1. Research on Intelligent Vehicle Environment Perception Technology Based on Multi-sensor Fusion

  • Research Background

Environmental perception is essential in the field of autonomous driving. Due to the advantages and disadvantages of different sensors, multi-sensor fusion has become a hot and challenging research topic in environmental perception. The research on multi-sensor fusion perception method will provide technical support for the construction of intelligent transportation and the development of automatic driving, and will play a significant and positive role in advancing the intelligent development of automobile industry.

  • Research Achievements

In order to tackle the difficulty of moving vehicle detection caused by the incomplete target contour in remote sparse point cloud, a moving vehicle detection algorithm based on PE-CPD is proposed. In this approach, the moving targets in the scenario are filtered by the adaptive threshold which changes with the distance of the target and the resolution of the virtual scanning mapping angle. Then, the pose of the target is estimated by using the point cloud registration information combined with the process of motion state calibration and model selection. And it is introduced into the process of moving vehicle detection. Experimental results show that the proposed method performs well in moving vehicle detection, especially in the remote sparse point cloud scenario.

To solve the problem of inaccurate ground segmentation in the case of undulating ground, occlusion of obstacles and sparse point cloud, a point cloud ground modeling algorithm based on hybrid regression technology is initiated. RLWR and gradient filter are used to establish the ground seed model along the scanning radial direction, to obtain the overall ground seed skeleton. Based on this, GPR is used to conduct the ground autonomous modeling along the scanning circumferential direction, and the ground circumferential continuity is used to make up for the modeling error caused by the lack of radial point cloud. The method uses the whole plane continuity to predict the height of the ground point cloud, which improves the robustness of the algorithm to the environment.

At the same time, a pedestrian detection algorithm based on single template matching is proposed. KDE pedestrian clustering algorithm based on hierarchical segmentation and multi-level fusion is used to solve the problem of under-segmentation of neighboring pedestrians. Secondly, the projection map of potential pedestrian clustering is constructed by main plane projection, from which the LARK feature matrix reflecting the target contour is extracted, and the similarity with the template is calculated to determine the target type. The algorithm avoids the need for a large number of training data, and can effectively distinguish pedestrians from other similar objects in the scenario.

  • Leading Research Member: LIU Kaiqi
  • Representative Papers
  1. Liu K, Wang W, Tharmarasa R, et al. Dynamic vehicle detection with sparse point clouds based on PE-CPD[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(5): 1964-1977.
  2. Liu K, Wang W, Wang J. Pedestrian Detection with Lidar Point Clouds Based on Single Template Matching[J]. Electronics, 2019, 8(7): 780.
  3. Liu K, Wang W, Tharmarasa R, et al. Ground surface filtering of 3D point clouds based on hybrid regression technique[J]. IEEE Access, 2019, 7: 23270-23284.
  4. Liu K, Wang J. Fast dynamic vehicle detection in road scenarios based on pose estimation with Convex-Hull model[J]. Sensors, 2019, 19(14): 3136.
  5. Liu K, Wang W. Pedestrian detection on the slope using multi-layer laser scanner[C]//2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017: 1-7.
  6. Liu K, Wang W, Sun Z. Recognition of SAR image based on combined templates[C]//2013 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2013: 284-287.

2. Optimal Sensor Selection and Deployment Planning Based on Multi-objective Optimization

  • Research Background

Due to the advantages of high computing efficiency and wide sensing range, Vehicle-Road-Cloud collaborative fusion sensing is an inevitable trend in the development of intelligent connected vehicle. Traditional researches on sensor deployment optimization often only optimize for a single index and variable and lack a reasonable road network model. Therefore, the abstract modeling of multi-sensor perception systems and urban roads, together with sensor-deployment-multi-objective optimization algorithms for perception system, have positive and important significance for the improvement of intelligent connected vehicle perception systems and the full use of resources.

  • Leading Research Member: LU Zhengye

3. Target Positioning and Tracking Based on Multi-sensor Information Fusion

  • Research Background

Intelligent connected vehicles rely on the data of sensors. In practical applications, different sensors have different working principles and application characteristics, which cannot be accurately applied in some scenarios. Therefore, multi-sensor fusion has become a consensus. The application of sensors in various information environments, to resist various types of noise interference, accurately identify targets, fuse asynchronous heterogeneous information , and obtain invariant features, is the focus of multi-sensor fusion research with great significance.

  • Research Achievements

In order to study how to reduce noise interference in multi-sensor fusion perception, improve the accuracy of positioning and tracking of targets after multi-sensor fusion, we need to analyze the source of the noise,improve the fusion algorithm for the noise of the sensor's original data and the noise caused by the unstable reference of the vehicle, and then reduce the positioning and tracking error by comparing the improved algorithm with the unimproved algorithm.

  • Leading Research Member: ZHU Shihao

 

Created: Mar 05, 2021 | 09:29