Cooperative localization based on topology matching

Abstract

In this paper, we propose a new vehicle localization method based on topology matching in mutli-vehicle environment. Each vehicle is assumed to generate a local map which is a set of position measurements of nearby vehicles by using onboard low-cost GPS and ranging sensors, and share it with others by broadcasting via vehicle-to-vehicle(V2V) communication. When a vehicle receives multiple local maps from neighbors, it incorporates and fuses them with its own local map by using a local map matching algorithm. The proposed algorithm is based on the topology matching technique and the multi-sensor Kalman filter. Simulation results show that our method can extend the detection range and improve the position accuracy by 65% compared to conventional localization methods utilizing the Kalman filter with only onboard GPS measurements.

Survey on vehicle map matching techniques

“…Incremental [89] The trajectory points are matched to subpaths by linear model Incremental Probability statistics [108] Set inference rules and weight them Global [90] Construct a search tree for the later selected sections and then evaluate the results using spatial and temporal information Global [104] The probability formula is used to search the candidate segment and the results are obtained by the correlation coefficient method Global [105] Using the Bayesian model to obtain the highest score path Global…”

“…They divided the matching process into four different states and implemented the corresponding matching algorithm according to each state. In [89], sensors are used to receive road network data for nearby vehicles. After using the map data of multiple vehicles to obtain the subpath, the matching position is figured out by the linear model.…”

Improving Localization Accuracy: Successive Measurements Error Modeling

“…The objective of cooperative positioning is to utilize the network localization resources by allowing neighboring nodes to work together to cooperatively improve the accuracy of their location via the periodic exchange of location information. The focus of research in this area is centered on three main themes: integrating ego measurements with measurements sent by neighbors [ 2 , 6 , 7 ], assessment of neighbors’ location measurements by analyzing the communication message characteristics [ 4 , 8 , 9 , 10 ], and integrating ego or neighbor measurements with map information [ 3 ]. Cooperative schemes were proposed to fuse data pertaining to multiple sensors within the vehicle together with data received from other neighbor vehicles in order to obtain relative position estimates [ 6 ].…”

“…Some schemes incorporate only GPS information to compute inter-vehicle distances for cooperative localization [ 7 ]. Local topology information was generated using GPS and ranging sensors, and then exchanged among vehicles [ 4 ]. Recently, the characteristics of the Dedicated Short-Range Communications (DSRC) standard were used to enhance the GPS position estimates for vehicles [ 8 ].…”

“…Research in localization accuracy is focused on the incorporation of additional data sources in the localization process in order to enhance the accuracy of a vehicle’s GPS measurements. Example localization techniques are the fusion of data from other sensors [ 2 ], the use of anchor landmarks and maps [ 3 ], and cooperative positioning, the verification and correction of location data among neighbor nodes [ 4 , 5 ]. However, deploying these methods may incur additional costs and increase the complexity of the localization scheme.…”