Autonomous Vehicle Queue Joining at Pickup and Drop-Off Locations
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Solution Overview
Problem
Autonomous vehicles lack the intuition to recognize and respond to queuing behaviors, often causing inconvenience and traffic congestion by improperly joining or exiting queues, which can lead to unsafe conditions for other road users.
Innovation Solution
The vehicle uses sensor data and machine learning models to determine the presence of a queue, assess potential disruptions, and make decisions to join or exit the queue independently, including identifying designated loading/unloading spots and predicting wait times, thereby reducing the need for remote operator intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles join queues at pickup and drop-off locations, then service capability is improved, but traffic congestion and blocking of other road users increases
Solution Approach 1:
The autonomous vehicle performs preliminary actions by detecting queue conditions, designated spots, and traffic patterns before joining the queue. The vehicle assesses whether to join the queue, identifies the optimal entry point, and determines the appropriate designated spot in advance, thereby avoiding impromptu queue joining that could cause traffic congestion.
Solution Approach 2:
The autonomous vehicle independently determines queue joining decisions, identifies designated spots, and executes queueing behaviors without human intervention. The vehicle uses sensor data and machine learning models to autonomously assess queue conditions, select appropriate actions, and manage its own queueing process, thereby adapting to dynamic traffic conditions while minimizing disruption.
2Extent of automation
If autonomous vehicles independently make queuing decisions using sensor data and machine learning, then remote operator intervention is reduced, but complexity of the autonomous decision-making system increases
Solution Approach 1:
The autonomous vehicle continuously receives sensor data from its environment, processes this information through machine learning models, and adjusts its queueing decisions based on feedback about queue conditions, traffic patterns, and designated spot locations. This closed-loop feedback system enables independent decision-making while managing complexity through iterative learning and adaptation.
Solution Approach 2:
The patent replaces manual queue management (mechanical human decision-making) with automated sensor-based detection and machine learning algorithms. The system substitutes human intuition and experience with electronic sensors, data processing, and algorithmic decision-making, thereby achieving independent operation while the complexity is managed through software-based solutions.
3Ease of operation
If autonomous vehicles fail to recognize queuing behaviors, then simplicity of vehicle operation is maintained, but inconvenience and unsafe conditions for other road users increase
Solution Approach 1:
The autonomous vehicle independently detects queue conditions, recognizes queuing behaviors of other vehicles, and makes appropriate queueing decisions without human assistance. The vehicle uses sensor data to identify queues, determine designated spots, and execute queueing maneuvers autonomously, thereby eliminating the need for human drivers while improving safety and reducing inconvenience to other road users.
Solution Approach 2:
The system continuously monitors sensor data to detect queue conditions and adjusts its behavior accordingly. By providing real-time feedback about queue presence, designated spot locations, and traffic patterns to the decision-making system, the vehicle can recognize queuing behaviors and adapt its operation to avoid causing inconvenience or unsafe conditions.
Data Source
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AI summary
Aspects of the disclosure provide for controlling an autonomous vehicle 100 to respond to queuing behaviors at pickup or drop -off locations. As an example, a request to pick up or drop off a passenger at a location may be received. The location may be determined to likely have a queue 710 for picking up and dropping off passengers. Based on sensor data received from a perception system 172, whether a queue exists at the location may be determined. Once it is determined that a queue exists, it may be determined whether to join the queue to avoid inconveniencing other road users. Based on the determination to join the queue, the vehicle may be controlled to join the queue.