Autonomous Vehicle Summon Navigation in Crowded Parking Lots
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Solution Overview
Problem
Human drivers face challenges in operating vehicles, particularly in navigating through 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 and no intelligence in navigating their own path.
Innovation Solution
An autonomous vehicle system that uses sensor data, such as camera 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
1Ease of operation
If remote operation with limited straight-line path is used, then device complexity is reduced, but ease of operation deteriorates in crowded environments
Solution Approach 1:
The vehicle performs self-navigation by autonomously planning its own path from the parking location to the target destination using sensor data and occupancy grids, eliminating the need for complex manual remote control operations in crowded environments
Solution Approach 2:
The system changes the operational parameters from simple straight-line remote control to complex autonomous navigation by implementing multi-sensor fusion, machine learning models, and dynamic path planning algorithms that adapt to environmental conditions
2Ease of operation
If autonomous navigation with sensor data processing is implemented, then ease of operation improves, but device complexity increases
Solution Approach 1:
The autonomous navigation system is segmented into distinct functional modules: sensor data acquisition, occupancy grid generation, path planning, and vehicle control, allowing complex navigation tasks to be managed through specialized subsystems
Solution Approach 2:
The vehicle's sensor suite and processing system serve multiple functions including environmental perception, obstacle detection, path planning, and real-time navigation adjustment, reducing overall system complexity through multi-functional integration
3Adaptability or versatility
If single straight-line path with limited steering is used, then device complexity is reduced, but adaptability deteriorates in complex environments
Solution Approach 1:
The navigation system dynamically adjusts the vehicle's path in real-time based on environmental conditions, obstacles, and target location, transitioning from static straight-line control to dynamic adaptive routing through crowded spaces
Solution Approach 2:
The system continuously receives feedback from sensors about the environment and vehicle position, using this information to adjust the occupancy grid and recalculate optimal paths, enabling adaptive navigation through complex and changing environments
4Productivity
If no intelligence in navigating is provided, then device complexity is reduced, but productivity deteriorates in retrieval tasks
Solution Approach 1:
The vehicle autonomously performs the complete retrieval task including self-navigation from parking to target, self-steering through crowded areas, and self-positioning without human intervention, dramatically improving retrieval productivity
Solution Approach 2:
The system replaces manual mechanical control operations with automated intelligence including machine learning models for path planning and sensor-based navigation, enabling efficient autonomous vehicle retrieval without human operators
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.