Self-Driving Vehicle Behavior Models for Doors and Elevators
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Solution Overview
Problem
Self-driving vehicles lack the ability to naturally and efficiently navigate through various structural elements like doors and elevators while following a human leader, leading to potential obstruction and inefficiency.
Innovation Solution
A method and system for a self-driving vehicle to implement behavior models that allow it to cooperatively navigate through structural elements by tracking and replicating human leader interactions, using sensors and data processing to execute appropriate stops, starts, and movements in response to environmental and human actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If self-driving vehicles use traditional navigation algorithms, then they can maintain simple control logic, but they fail to naturally navigate through structural elements like doors and elevators while following human leaders
Solution Approach 1:
The system captures human leader behavior through motion capture technology and replicates these movements in the self-driving vehicle's control system. By copying human navigation patterns through doors, elevators, and other structural elements, the vehicle gains natural navigation capabilities without requiring complex decision-making algorithms for each scenario
Solution Approach 2:
Human leader behaviors are captured and stored in advance through motion capture sessions. This preliminary action creates a library of navigation patterns that the vehicle can directly apply during operation, eliminating the need for real-time complex reasoning about how to navigate various structural elements
2Ease of operation
If self-driving vehicles follow human leaders closely, then they can provide good companionship and cooperation, but they may obstruct the human leader's movement and create inefficiency
Solution Approach 1:
The system continuously monitors the human leader's movements and adjusts the vehicle's position and speed in real-time based on this feedback. By detecting when the leader slows down or changes direction, the vehicle can dynamically adjust its following behavior to maintain cooperation while avoiding obstruction
Solution Approach 2:
The vehicle's following behavior is made dynamic rather than static. It continuously adapts its distance, speed, and positioning based on the leader's current state, allowing the system to transition between maintaining close proximity for cooperation and creating distance to avoid obstruction
3Adaptability or versatility
If self-driving vehicles implement comprehensive behavior models for all structural elements, then they can achieve natural navigation, but the system complexity and data requirements increase significantly
Solution Approach 1:
The navigation system is divided into separate behavior models for different structural elements (doors, elevators, staircases, etc.). Each behavior model handles a specific type of structure independently, allowing the system to achieve comprehensive adaptability while keeping individual model complexity manageable through modular design
Data Source
AI summary
Provided is a method of modeling behavior for a self-driving vehicle, e.g., as a follower vehicle. Also provided is a vehicle configured to execute the behavior model to cooperatively navigate at least one structural element in an environment. The structural element can be or include a door, a vestibule, and/or an elevator, as examples. The behavior model can be formed by a method that includes tracking and measuring leader-follower interactions and actions with at least one structural element of an environment, representing the leader behaviors and the follower behavior in a behavior model, and electronically storing the behavioral model. The leader-follower interactions and actions can include leader behaviors and follower behaviors, including starts, stops, pauses, and movements of the leader, follower vehicle, and/or objects.