Vehicle lane position estimation for link level in-vehicle navigation and automatic vehicle location.

摘要

Vehicle navigation is performed at three separate scales: a macroscale level that considers route planning and guidance; a microscale level that conducts low-level lateral and longitudinal vehicle control; and a mesoscale level that performs maneuvers at the lane level. Most work to date has been at the macro- and micro-scale levels; very little has been done at the mesoscale due to the lack of a reliable lane-level vehicle positioning system that can determine vehicle lane position for safety and assistance of vehicle maneuvers among multiple road lanes.; This dissertation presents a unique vehicle lane position estimation methodology that locates a vehicle's lane position in real time by mapping the spatial position measurements onto the specific lane the vehicle is running on with reference to a lane enhanced digital map. Further, this methodology has been implemented as a system and evaluated in the real-world. This dissertation has several key contributions: (1) A low-cost lane-level positioning system has been designed, developed, and implemented with 1 to 2 meter positioning accuracy; (2) A new lane-level digital map construction technique has been developed, as well as the definition of a lane-level digital roadway database structure; (3) New lane-level map-matching algorithms have been developed and implemented based on a weight-modified curve-to-curve mapping method and a Bayesian probabilistic lane estimating technique; (4) A new method to estimate lane traffic velocity has been developed using the lane-level positioning capability.; The developed system has been tested on a number of roadways and has performed very well when used with accurately surveyed map data. Based on the results of over 100,000 seconds, the developed system has correctly determined in real time the vehicle's lane position 97% of the time. The lane-level positioning capability opens up the door for a large number of mesoscale navigational tasks in the intelligent transportation system arena including lane maneuver planning and execution, vehicle safety systems, better fleet management, and lane-based traffic measurements from probe vehicles. Future work in lane-level vehicle maneuver modeling and planning is described.