Autonomous Vehicle Fallback Context Evaluation
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Solution Overview
Problem
Mid-level and fully autonomous vehicles face challenges when they cannot transfer control back to an occupant due to unavailability, such as occupant fatigue or lack of licensed operators, as they lack dynamic default operations tailored to the specific context, often resorting to static actions like pulling over or stopping without considering the surrounding environment.
Innovation Solution
The autonomous vehicle determines its operational context through various factors like weather, occupant state, and traffic conditions, and performs a progression of default actions suited to the identified context, such as navigating to a safe location, contacting companion vehicles for assistance, or alerting authorities, rather than relying on static default operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the autonomous vehicle performs a single default action (e.g., transitioning control back to occupant or coming to a full stop) regardless of context, then the system is simple to operate, but the adaptability to different driving contexts deteriorates
Solution Approach 1:
The patent implements dynamic default actions that automatically adapt to different driving contexts. The system evaluates multiple factors including traffic conditions, weather, occupancy status, and vehicle state to dynamically select from a progression of default actions ranging from gentle warnings to full emergency stops, eliminating the need for manual system reconfiguration while maintaining operational simplicity
Solution Approach 2:
The system changes operational parameters based on contextual evaluation. By monitoring multiple input parameters (traffic density, weather conditions, occupant availability, vehicle speed) and adjusting the default action selection accordingly, the system achieves high adaptability without requiring complex manual intervention, as the parameters automatically drive the selection of appropriate responses
2Reliability
If the autonomous vehicle comes to a full stop in the middle of the street when the occupant is unavailable, then the safety is improved, but the disruption to traffic flow worsens
Solution Approach 1:
The system dynamically adjusts the default action based on real-time traffic context. When an occupant is unavailable, the system evaluates traffic conditions, vehicle location, and environmental factors to select from a progression of actions including gradual deceleration, pulling over to safe locations, or controlled stops, rather than always performing abrupt full stops that disrupt traffic flow
Solution Approach 2:
The system prepares compensatory measures in advance by evaluating multiple contextual factors before an emergency situation occurs. By pre-assessing traffic conditions, available safe locations, and vehicle state, the system can select default actions that minimize traffic disruption while ensuring safety, such as gradually slowing down or moving to a safe pull-over location before stopping
3Adaptability or versatility
If the autonomous vehicle performs context-specific default actions, then the adaptability to driving context is improved, but the device complexity worsens
Solution Approach 1:
The patent segments the context evaluation into multiple independent modules, each assessing specific factors such as traffic conditions, weather, occupancy status, and vehicle state. This modular segmentation allows the system to achieve high adaptability through coordinated evaluation of discrete components rather than requiring a monolithic complex system, making the architecture more manageable and maintainable
Solution Approach 2:
The system implements a universal default action framework that handles multiple driving contexts and scenarios through a single multi-functional evaluation process. By designing the context evaluation system to universally assess various factors (traffic, weather, occupancy, vehicle state) and select from a standardized progression of default actions, the system achieves high adaptability without proportionally increasing complexity
Data Source
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AI summary
A vehicle comprising a processor and a memory may be configured to perform default operations. The processor may determine, while currently operating autonomously, a current context in which the vehicle is operating autonomously. The memory may store the current context. The processor may further determine, while currently operating autonomously and based on the current context, one of a plurality of operations to perform when the vehicle is unable to continue to operate autonomously, and unable to transfer control of the vehicle to an occupant, and perform the determined one of the plurality of operations.