Dynamic Lane-level vehicle navigation with lane group identification

Abstract

The disclosure relates to dynamic Lane-level vehicle navigation with lane group identification. The disclosure includes a system and method for dynamic vehicle navigation with lane group identification. The system includes one or more processors configured to identify a plurality of roadway lanes on a plurality of roads, determine a plurality of lane groups in the plurality of roadway lanes basedon traffic data indicating traffic on the plurality of roadway lanes, and estimate lane-group level traffic of the plurality of lane groups based on the traffic on the plurality of roadway lanes. Theprocessors may further identify a client device location of a client device and a destination location, optimize a route between the client device location and the destination location using the plurality of roads and based on the lane-group level traffic of the plurality of lane groups, and provide route guidance to the client device based on the optimized route.

Description

technical field [0001] The present disclosure relates to a navigation system for estimating travel time of a travel route. In particular, the specification relates to navigation systems that utilize traffic in lane groups to improve the accuracy of estimated travel times. Background technique [0002] Users are increasingly relying on navigation systems on computing devices to provide useful information while on the road. However, existing systems generally only consider the average volume of routes or roads, and generally do not consider lane-level navigation or traffic. [0003] In some road networks, due to the vehicle demand for various pairs of origin and destination points, and traffic signals throughout the network, even on the same road segment, the traffic conditions of different lanes (such as vehicle queuing, vehicle delay and average speed) can also vary greatly. However, current navigation systems cannot adequately cope with these changes. [0004] Some exis...

AI Technical Summary

Problems solved by technology

However, Tian's prediction method is only suitable for uninterrupted highway segments that are not scalable to arterial road networks, thus limiting the widespread application of the method in real-world navigation systems

In addition, due to the simplicity of the method, the method cannot capture different individual vehicle behaviors on restricted highway segments, such as merging

Also, Tian does not account for lane groups, resulting in the same unnecessary lane changes as explained above for '052

[0008] Thus, using only lane-level traffic data is not sufficient for effective navigation with lane-level guidance

Images