LaIF: A Lane-Level Self-Positioning Scheme for Vehicles in GNSS-Denied Environments

• 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

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

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)