LIDAR Based Vehicle Classification
| 项目名称： | LIDAR Based Vehicle Classification |
| 摘要： | The proposed research will continue investigation into the use of LIDAR (Light Detection And Ranging) sensors to scan vehicles passing at speed and develop the necessary algorithms to classify the vehicles accurately. Both probe vehicle mounted LIDAR and wayside mounted LIDAR deployments will be considered. A van instrumented with a pair of LIDAR sensors that sweep vertically across the road in a "speed-trap" configuration will be employed. The probe vehicle based classification data will be collected while the van is driving on pre-defined tours while the wayside mounted classification data will be collected while the van is parked at the study location to collect data over several hours. This proposal picks up where the earlier studies leave off advancing the work on the following fronts: shape based classification, developing the LIDAR classification over a wider range of Federal Highway Administration (FHWA) axle classes, improving occlusion reasoning, and axle detection and inference. LIDAR based classification is a promising alternative to supplement existing classification stations. Such an installation could be permanent or temporary, would cost significantly less than a comparable in-pavement system, and will require significantly less setup in the field. Compared to conventional sensors, the LIDAR collect much more information about the passing vehicles. The mobile, probe vehicle mounted LIDAR promises to provide lower resolution information on many more links than are currently available from the fixed classification stations. Such information will help detect problem spots earlier and validate assumptions of transferability of measurements from the fixed locations to other parts of the network. |
| 状态： | Completed |
| 资金： | 64683.00 |
| 资助组织： | Ohio Transportation Consortium |
| 执行机构： | Ohio State University, Columbus |
| 开始时间： | 20101001 |
| 实际结束时间： | 20110930 |
| 主题领域： | Data and Information Technology；Highways；Operations and Traffic Management |
相关文献
- Investigation of Truck Data Collection using LiDAR sensing technology along Rural Highways
- Investigation of LiDAR sensing technology to Improve Freeway Traffic Monitoring
- DeepHyd: A Deep Learning-based Artificial Intelligence Approach for the Automated Classification of Hydraulic Structures from LiDAR and Sonar Data
- A Vision-based Traffic Monitoring System for Vehicle Detection and Vehicle Classification
- Refining Inductive Loop Signature Technology for Statewide Vehicle Classification Counts
- Optimal Loop Placement and Models for Length-Based Vehicle Classification and Stop-and-Go Traffic
检索历史
应用推荐
