Vehicle positioning in urban environments using particle filtering-based global positioning system, odometry, and map data fusion

This article presents a new method for land vehicle navigation using global positioning system (GPS), dead reckoning sensor (DR), and digital road map information, particularly in urban environments where GPS failures can occur. The odometer sensors and map measure can be used to provide continuous navigation and correct the vehicle location in the presence of GPS masking. To solve this estimation problem for vehicle navigation, we propose to use particle filtering for GPS/odometer/map integration. The particle filter is a method based on the Bayesian estimation technique and the Monte Carlo method, which deals with non-linear models and is not limited to Gaussian statistics. When the GPS sensor cannot provide a location due to the number of satellites in view, the filter fuses the limited GPS pseudo-range data to enhance the vehicle positioning. The developed filter is then tested in a transportation network scenario in the presence of GPS failures, which shows the advantages of the proposed approach for vehicle location compared to the extended Kalman filter

Automated updating of road network databases: road segment grouping using snap-drift neural network

Presented in this paper is a major step towards an innovative solution of GIS road network databases updating which moves away from existing traditional methods where vendors of road network databases go through the time consuming and logistically challenging process of driving along roads to register changes or GIS road network update methods that are exclusively tied to remote sensing images. Our proposed road database update solution would allow users of GIS road network dependent applications (e.g. in-car navigation system) to passively collect characteristics of any “unknown route” (roads not in the database) on behalf of the provider. These data are transferred back to the provider and inputted into an artificial neural net (ANN) which decides, along with similar track data provided by other service users, whether to automatically update (add) the “unknown road” to the road database on probation allowing subsequent users to see the road on their system and use it if need be. At a later stage when there is enough certainty on road geometry and other characteristics the probationary flag could be lifted and permanently added to the road network database. Towards this novel approach we mimicked two journey scenarios covering two test sites and aimed to group the road segments from the journey into their respective road types using the snap-drift neural network (SDNN). The performance of the SDNN is presented and its potential in the proposed solution is investigated

Updating of Road Network Databases: Spatio-Temporal Trajectory Grouping Using Snap-Drift Neural Network

Research towards an innovative solution to the problem of automated updating of road network databases is presented. It moves away from existing methods where vendors of road network databases either go through the time consuming and logistically challenging process of driving along roads to register changes or use update methods that rely on remote sensing images. The solution presented here would allow users of road network dependent applications (e.g. in-car navigation system or NavSat) to passively collect characteristics of any “unknown route” (departure from the known roads in the database) on behalf of the provider. These data would be processed either by an onboard neural network or transferred back to the NavSat provider and input to a neural net (ANN) along with similar track data provided by other service users, to decide whether or not to automatically update (add) the “unknown road” to the road database. This would be performed ‘on probation’, allowing subsequent users to see the road on their system and use it if need be. At a later stage, when sufficient information on road geometry and other characteristics has accumulated in order to have confidence in the classification, the probationary flag would be lifted and the new road permanently added to the road network database. To investigate this novel approach, GPS-based trajectory data collected in London are analysed using a Snap-Drift Neural Network (SDNN) and categorised into different road class segments. The performance of the SDNN and the key variables required are presented