Aptiv Technologies Limited

Aptiv Technologies Ag

Steven G Malson

James Nicholas Nickolaou

Yusuf Zohar Dilawar

Neha N Gupta

The techniques and systems herein enable robot management in populated areas. Specifically, a robot management system receives, from a robot system, a requested route and time for the robot system to travel. A dynamic map that includes dynamic and temporal information about an area of the requested route is then used to determine whether the requested route and time are approved, approved with a modified route or time, or disapproved. An approval message, a modified approval message, or a disapproval message is then generated and sent to the robot system. By using the described techniques, municipalities can monitor, manage, and regulate robots across various operators and locations. Doing so may prevent a high density of robots along various routes, disruptions to the services of such robots, and/or disruptions to surrounding pedestrians and vehicles.

Robot management in populated areas

This document describes techniques for localizing a vehicle to map data, for example, to enable assisted driving and automated driving within a road. A road model builder is described that uses vehicle location data to determine its position relative to map data obtained from a provider, which can be different than the location data's source. Using the vehicle position, the road model builder maintains a current lane segment for the vehicle and its relative position within the current lane segment. Based on its relative position, the road model builder determines whether a transition to another lane segment is appropriate before setting the current lane segment to be the other lane segment. The current lane segment, when provided as an input to an assisted-driving or automated-driving system, can enable safer operation of the vehicle and use fewer computing resources than other systems.

Vehicle localization to map data

This document describes techniques, apparatuses, and systems for standard-definition (SD) to high-definition (HD) navigation route determination. An example route builder receives a navigation route generated for a host vehicle. The navigation route includes a list of waypoints generated by an SD map database. The route builder matches the list of waypoints to lane geometry data maintained in an HD map database and outputs HD navigation route to a vehicle controller. The HD navigation route includes the list of waypoints, additional waypoints, and lane geometry data. The vehicle controller can then operate the host vehicle in a roadway environment along the HD navigation route. In this way, the described techniques and systems can provide HD navigation routes required by some assisted-driving and autonomous-driving systems based on SD navigation routes.

Standard-definition to high-definition navigation route determination

This document describes vehicle localization using map and vision data. For example, this document describes a localization system that obtains a map centerline point and a vision centerline point of a lane of a roadway. The localization system also obtains the position of the vehicle. The localization system can then compare the map centerline point and the vision centerline point to generate a lateral and a longitudinal correction relative to the vehicle's position. The lateral and longitudinal corrections are used to generate a corrected position. In this way, the described localization system can provide accurate vehicle localization that addresses potential drift caused by lapsed or inaccurate positioning data and allows for the operation of assisted-driving and autonomous-driving systems at higher speeds and on roadways with tighter curves.

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Metamora, MI, United States of America

Steven G Malson