Autonomous Vehicle Fallback Task Triggers for Safe Self-Recovery
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Solution Overview
Problem
Autonomous vehicles lack effective mechanisms to manage fallback tasks in situations where normal operation is compromised, such as vehicle malfunctions or low fuel levels, leading to potential stranding and inefficiencies in maintenance and operation.
Innovation Solution
A system comprising processors and memory that execute primary and fallback tasks based on predefined triggers, allowing vehicles to autonomously navigate to safe locations for maintenance or refueling, with a fleet management system that dispatches vehicles and updates tasks as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles operate without fallback task mechanisms, then operational simplicity is maintained, but vehicle reliability deteriorates when malfunctions or low fuel levels occur
Solution Approach 1:
The system pre-configures fallback tasks and their associated triggers before operational needs arise. When conditions such as low fuel levels or system malfunctions occur, the corresponding fallback tasks are automatically activated based on pre-established trigger conditions, eliminating the need for complex real-time decision-making while ensuring reliable fallback operations
Solution Approach 2:
The operational system is divided into distinct primary tasks and fallback tasks, each with specific trigger conditions. This segmentation allows the vehicle to operate normally under primary tasks while having isolated, pre-planned fallback procedures for specific failure modes, reducing overall system complexity while improving reliability
2Reliability
If fallback tasks are implemented to handle malfunctions, then vehicle reliability improves, but operational efficiency deteriorates due to task interruptions
Solution Approach 1:
The system dynamically adjusts task execution based on real-time monitoring of trigger conditions. Fallback tasks are activated only when specific conditions are met, and the system can transition between primary and fallback tasks smoothly, minimizing operational disruptions while maintaining reliability
Solution Approach 2:
The system continuously monitors vehicle status and compares it against predefined trigger conditions. This feedback mechanism ensures that fallback tasks are activated only when necessary, preventing unnecessary interruptions to primary operations while ensuring reliability when actual malfunctions occur
3Adaptability or versatility
If multiple fallback tasks are configured for different scenarios, then adaptability improves, but device complexity increases
Solution Approach 1:
Multiple fallback tasks with different trigger conditions are pre-configured for various malfunction scenarios. The system establishes a mapping between trigger conditions and corresponding fallback tasks before operation, allowing the vehicle to adapt to different scenarios automatically without requiring complex real-time analysis or decision-making
Solution Approach 2:
The fallback task management system serves multiple functions: it monitors various vehicle parameters, evaluates multiple trigger conditions, selects appropriate fallback tasks, and manages task execution. This universal fallback mechanism handles diverse scenarios through a unified framework, improving adaptability while controlling complexity
Data Source
AI summary
Aspects of the present disclosure relate to a system having a memory, a plurality of self-driving systems for controlling a vehicle, and one or more processors. The processors are configured to receive at least one fallback task in association with a request for a primary task and at least one trigger of each fallback task. Each trigger is a set of conditions that, when satisfied, indicate when a vehicle requires attention for proper operation. The processors are also configured to send instructions to the self-driving systems to execute the primary task and receive status updates from the self-driving systems. The processors are configured to determine that a set of conditions of a trigger is satisfied based on the status updates and send further instructions based on the associated fallback task to the self-driving systems.