Name
Abstract | ||
|---|---|---|
Vehicle self-positioning is of significant importance for intelligent transportation applications. However, accurate positioning (e.g., with lane-level accuracy) is very difficult to obtain due to the lack of measurements with high confidence, especially in an environment without full access to a global navigation satellite system (GNSS). In this paper, a novel information fusion algorithm based on a particle filter is proposed to achieve lane-level tracking accuracy under a GNSS-denied environment. We consider the use of both coarse-scale and fine-scale signal measurements for positioning. Time-of-arrival measurements using the radio frequency signals from known transmitters or roadside units, and acceleration or gyroscope measurements from an inertial measurement unit (IMU) allow us to form a coarse estimate of the vehicle position using an extended Kalman filter. Subsequently, fine-scale measurements, including lane-change detection, radar ranging from the known obstacles (e.g., guardrails), and information from a high-resolution digital map, are incorporated to refine the position estimates. A probabilistic model is introduced to characterize the lane changing behaviors, and a multi-hypothesis model is formulated for the radar range measurements to robustly weigh the particles and refine the tracking results. Moreover, a decision fusion mechanism is proposed to achieve a higher reliability in the lane-change detection as compared to each individual detector using IMU and visual (if available) information. The posterior Cramer-Rao lower bound is also derived to provide a theoretical performance guideline. The performance of the proposed tracking framework is verified by simulations and real measured IMU data in a four-lane highway. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tits.2018.2870048 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Radar tracking,Atmospheric measurements,Particle measurements,Global navigation satellite system,Acceleration | Radar,Computer vision,Extended Kalman filter,Radar tracker,Particle filter,Ranging,GNSS applications,Inertial measurement unit,Artificial intelligence,Intelligent transportation system,Engineering | Journal |
Volume | Issue | ISSN |
20 | 8 | 1524-9050 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramtin Rabiee | 1 | 5 | 3.11 |
| Xionghu Zhong | 2 | 152 | 14.61 |
| Y.S. Yong | 3 | 34 | 4.90 |
| Wee-Peng Tay | 4 | 128 | 10.36 |