Autonomous Vehicle Summon Path Planning in Crowded Parking Lots
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Solution Overview
Problem
Human drivers face challenges in conveniently and efficiently navigating vehicles, especially in crowded areas like parking lots, due to the limitations of existing remotely operated vehicles that can only follow a single straight path with limited steering range and no intelligence in navigating their own path.
Innovation Solution
An autonomous vehicle system that uses sensor data, such as vision, radar, and machine learning models to generate an occupancy grid representing drivable and non-drivable spaces, allowing the vehicle to plan and navigate an optimal path to a specified destination, while continuously updating its environment representation and performing safety checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If remote operation with limited steering range is used, then vehicle control is simplified, but navigation capability in crowded areas deteriorates
Solution Approach 1:
The vehicle autonomously navigates to the target location using its own sensors and processing systems. The processor determines the target location from sensor data and controls the vehicle to travel along the determined path without continuous human intervention, allowing the vehicle to serve itself in navigation tasks.
Solution Approach 2:
The patent replaces manual mechanical steering control with an autonomous system that uses sensor data processing and automated path determination. The processor substitutes for the human driver's mechanical control decisions by automatically determining optimal paths and controlling vehicle movement based on sensor inputs.
2Ease of operation
If a single straight-line path is used for remote operation, then control simplicity is improved, but path intelligence and navigation efficiency deteriorate
Solution Approach 1:
The vehicle's path is dynamically determined based on real-time sensor data and environmental conditions. The processor continuously analyzes sensor inputs and adjusts the travel path accordingly, allowing the vehicle to adapt its route dynamically rather than following a fixed straight-line path, improving navigation efficiency while maintaining automated control.
3Adaptability or versatility
If autonomous navigation with environment representation is implemented, then navigation intelligence is improved, but system complexity increases
Solution Approach 1:
The autonomous navigation system is segmented into distinct functional components: sensor data acquisition, environment representation generation, target location determination, and path control. The processor handles specific tasks of determining target location and controlling vehicle travel along the determined path, dividing the complex autonomous navigation function into manageable segments.
4Ease of operation
If manual vehicle retrieval from crowded parking areas is performed, then operational control is maintained, but time consumption and convenience deteriorate
Solution Approach 1:
The system performs preliminary actions by determining the target location and planning the navigation path before the vehicle begins movement. The processor analyzes sensor data to identify the target location and determines the optimal path in advance, allowing the vehicle to execute the pre-planned autonomous navigation without time-consuming manual intervention during the actual retrieval process.
Data Source
AI summary
A processor coupled to memory is configured to receive an identification of a geographical location associated with a target specified by a user remote from a vehicle. A machine learning model is utilized to generate a representation of at least a portion of an environment surrounding the vehicle using sensor data from one or more sensors of the vehicle. At least a portion of a path to a target location corresponding to the received geographical location is calculated using the generated representation of the at least portion of the environment surrounding the vehicle. At least one command is provided to automatically navigate the vehicle based on the determined path and updated sensor data from at least a portion of the one or more sensors of the vehicle.