Search: WFRF:(Rabiee Ramtin) >
LaIF: A Lane-Level ...
Abstract
Subject headings
Close
- 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 Cramér-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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- acceleration measurement
- decision theory
- distance measurement
- geographic information systems
- gyroscopes
- inertial navigation
- intelligent transportation systems
- Kalman filters
- nonlinear filters
- object detection
- object tracking
- particle filtering (numerical methods)
- radiofrequency measurement
- radionavigation
- road vehicles
- sensor fusion
- time-of-arrival estimation
- lane-level self-positioning scheme
- GNSS-denied environment
- vehicle self-positioning
- intelligent transportation applications
- information fusion algorithm
- particle filter
- lane-level tracking accuracy
- fine-scale signal measurements
- time-of-arrival measurements
- roadside units
- inertial measurement unit
- extended Kalman filter
- lane-change detection
- radar ranging
- high-resolution digital map
- lane changing behaviors
- radar range measurements
- decision fusion mechanism
- four-lane highway
- tracking framework
- Cramer-Rao lower bound
- visual information
- reliability
- multihypothesis model
- probabilistic model
- gyroscope measurements
- coarse-scale signal measurements
- LaIF
- IMU data
- vehicle position estimation
- transmitters
- radiofrequency signals
- Global Navigation Satellite System
- Radar tracking
- Atmospheric measurements
- Particle measurements
- Acceleration
- Vehicle localization
- GNSS-denied
- lane-level accuracy
- information fusion
- inertial navigation systems
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
To the university's database