Self-Powered Intelligent Traffic Monitoring Using IR Lidar and Camera
| dc.contributor.author | Tian, Yi | en |
| dc.contributor.committeechair | Furukawa, Tomonari | en |
| dc.contributor.committeemember | Ben-Tzvi, Pinhas | en |
| dc.contributor.committeemember | Asbeck, Alan T. | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2017-02-07T09:01:02Z | en |
| dc.date.available | 2017-02-07T09:01:02Z | en |
| dc.date.issued | 2017-02-06 | en |
| dc.description.abstract | This thesis presents a novel self-powered infrastructural traffic monitoring approach that estimates traffic information by combining three detection techniques. The traffic information can be obtained from the presented approach includes vehicle counts, speed estimation and vehicle classification based on size. Two categories of sensors are used including IR Lidar and IR camera. With the two sensors, three detection techniques are used: Time of Flight (ToF) based, vision based and Laser spot flow based. Each technique outputs observations about vehicle location at different time step. By fusing the three observations in the framework of Kalman filter, vehicle location is estimated, based on which other concerned traffic information including vehicle counts, speed and class is obtained. In this process, high reliability is achieved by combing the strength of each techniques. To achieve self-powering, a dynamic power management strategy is developed to reduce system total energy cost and optimize power supply in traffic monitoring based on traffic pattern recognition. The power manager attempts to adjust the power supply by reconfiguring system setup according to its estimation about current traffic condition. A system prototype has been built and multiple field experiments and simulations were conducted to demonstrate traffic monitoring accuracy and power reduction efficacy. | en |
| dc.description.abstractgeneral | This thesis presents a novel traffic monitoring system that does not require external power source. The traffic monitoring system is able to collect traffic variables including count, speed and vehicle types. The system uses two types of sensors and implements three different measuring techniques. By combining the results from the three techniques, higher accuracy and reliability is achieved. A power management component is also developed for the system to save energy usage. Based on current or predicted system power state, the power manager selectively deactivates or turns off certain part of the system to reduce power consumption. A system prototype has been built and multiple field experiments and simulations were conducted to demonstrate traffic monitoring accuracy and power reduction efficacy. The experiments have shown that the system achieves high accuracy in every variable estimation and large portion of energy is saved by adopting power management. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:9538 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/74949 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Infrastructural Traffic Monitoring | en |
| dc.subject | Multi-sensor Fusion | en |
| dc.subject | Kalman Filter | en |
| dc.subject | Dynamic Power Management | en |
| dc.title | Self-Powered Intelligent Traffic Monitoring Using IR Lidar and Camera | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
Files
Original bundle
1 - 1 of 1