Parking Area Mapping With Geofences for Autonomous Valet
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Solution Overview
Problem
Current self-driving vehicles struggle with navigating complex urban environments, particularly in poorly labeled and crowded city streets, limiting their ability to provide seamless autonomous parking and retrieval services.
Innovation Solution
A system and method for mapping parking areas using a computing device that receives survey data from a remote device with locating sensors to generate a parking map, including geofences and waypaths, enabling autonomous vehicles to navigate from a drop-off location to a parking spot and back to a pick-up location, allowing for automatic valet services at various points of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated driving systems are deployed in current self-driving vehicles, then automation capability is improved, but reliability deteriorates due to inability to handle complex urban environments and poorly labeled streets
Solution Approach 1:
The system performs preliminary mapping and surveying of parking areas before autonomous parking operations. Remote devices with locating sensors pre-survey the environment to create detailed maps including geofences and waypaths, allowing the automated vehicle to rely on pre-established spatial data rather than real-time perception in complex environments.
Solution Approach 2:
The patent introduces remote surveying devices as intermediaries that collect environmental data and create parking area maps. These maps serve as intermediary information structures that bridge the gap between the automated vehicle and the complex physical environment, providing reliable spatial guidance without requiring the vehicle to directly interpret complex urban scenes.
2Measurement precision
If comprehensive survey data collection is performed using remote devices with locating sensors, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system extracts the surveying function from the autonomous vehicle itself and places it in separate remote devices. This allows comprehensive data collection to be performed by dedicated surveying equipment while the vehicle relies on the processed mapping information, reducing the complexity burden on the vehicle system.
Solution Approach 2:
Survey data collection is performed in advance by remote devices before the autonomous parking operation. This preliminary action creates a detailed digital representation of the parking area that can be stored and referenced, achieving high measurement precision without requiring complex real-time surveying equipment in the vehicle.
3Reliability
If detailed parking maps with geofences and waypaths are generated, then navigation reliability is improved, but information processing time increases
Solution Approach 1:
The parking maps including geofences and waypaths are generated in advance during the survey phase, not during the actual parking operation. This preliminary map creation allows the vehicle to use pre-processed navigation data, achieving reliable path following without time-consuming real-time computation.
Solution Approach 2:
The navigation problem is segmented into map creation and path following phases. The complex task of navigating an unknown environment is divided into: (1) preliminary survey and map generation by remote devices, and (2) simple path execution by the autonomous vehicle using pre-defined waypaths and geofences.
Data Source
AI summary
Aspects relate to systems and methods for mapping a parking area for autonomous parking. An exemplary method includes receiving a point of interest designator for a point of interest, a drop-off location designator for a drop-off location, a parking location designator for a parking location, and a parking path designator for a parking path between the drop-off location and the parking location, receiving survey data of the point of interest from a remote device having at least a locating sensor, wherein survey data includes a drop-off geofence for the drop-off location, a parking geofence for the parking location, and a parking waypath for the parking path, and generating a parking map for the point of interest, wherein the parking map includes the drop-off location designator, the parking location designator, the parking path designator, the drop-off geofence, the parking geofence, and the parking waypath.