Autonomous Vehicle Stationary State Classification for Safer Maneuvering
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Solution Overview
Problem
Autonomous vehicles face challenges in determining whether a stationary vehicle is likely to remain stationary for an extended period, which is crucial for safe maneuvering and route planning.
Innovation Solution
The method involves detecting stationary vehicles using sensors and cameras, identifying visible indicia such as lights or open doors, and determining the type of stationary state (short-term or long-term) based on these indicia and traffic control factors, thereby informing the control strategy of the autonomous vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle uses basic detection methods to identify stationary vehicles, then the detection process is simple and fast, but the accuracy in determining whether the vehicle is short-term or long-term stationary is insufficient
Solution Approach 1:
The patent segments the stationary vehicle detection problem into multiple analysis dimensions: visible indicia detection (lights, doors, hoods), traffic control factor identification (stop signs, traffic lights, crosswalks), and temporal analysis (duration of stationary state). Each dimension is analyzed separately and then integrated to determine the overall stationary state, improving accuracy without overwhelming system complexity.
Solution Approach 2:
The system performs preliminary detection of visible indicia and traffic control factors before making the final determination of stationary state. By pre-identifying key features such as emergency lights, open doors, or stop signs in advance, the system prepares classification data that speeds up the final decision-making process while maintaining high accuracy.
2Reliability
If the autonomous vehicle analyzes multiple visible indicia and traffic control factors to determine stationary state, then the accuracy of navigation decisions is improved, but the processing time and computational load increase
Solution Approach 1:
The patent applies local quality by assigning different weights and priorities to different visible indicia and traffic control factors based on their reliability and relevance. For example, emergency lights or stop signs are given higher priority than minor indicators. This allows the system to focus computational resources on the most critical factors, reducing processing time while maintaining high reliability in navigation decisions.
3Object-affected harmful factors
If the autonomous vehicle implements comprehensive analysis of visible indicia and traffic control factors, then the safety of maneuvering around stationary vehicles is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent introduces an intermediary classification layer that processes visible indicia and traffic control factors before passing them to the navigation control system. This intermediary layer categorizes stationary vehicles into standardized states (short-term vs. long-term), simplifying the information that the navigation system must process while maintaining comprehensive safety analysis.
4Speed
If the autonomous vehicle uses simple detection methods, then the response speed is fast, but the ability to differentiate between short-term and long-term stationary vehicles is insufficient
Solution Approach 1:
The system performs preliminary detection of key features such as emergency lights, open doors, or stop signs in advance, preparing classification data that speeds up the final decision-making process. This allows the system to maintain fast response speeds while achieving high precision in differentiating between short-term and long-term stationary vehicles.
Solution Approach 2:
The patent changes the parameter of time by introducing temporal analysis of the stationary state duration. By monitoring how long a vehicle has remained stationary and combining this with visible indicia analysis, the system can accurately classify vehicles as short-term or long-term stationary without significantly increasing response time, as the temporal parameter is continuously updated during detection.
Data Source
AI summary
Aspects of the disclosure relate to an autonomous vehicle that may detect other nearby vehicles and designate stationary vehicles as being in one of a short-term stationary state or a long-term stationary state. This determination may be made based on various indicia, including visible indicia displayed by the detected vehicle and traffic control factors relating to the detected vehicle. For example, the autonomous vehicle may identify a detected vehicle as being in a long-term stationary state based on detection of hazard lights being displayed by the detected vehicle, as well as the absence of brake lights being displayed by the detected vehicle. The autonomous vehicle may then base its control strategy on the stationary state of the detected vehicle.