Open Door Detection Using Vehicle Orientation Cues
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast volume of data they need to process and store, which can limit or adversely affect navigation, especially when relying on traditional mapping technology.
Innovation Solution
A system and method for autonomous vehicle navigation that includes a processing unit, image capture devices, and a sparse map generation system to identify and respond to open vehicle doors, using image analysis and navigational actions based on orientation indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used for autonomous vehicle navigation, then the vehicle can navigate using established map data, but the vast volume of data needed to store and update the map poses daunting challenges and adversely affects navigation
Solution Approach 1:
The patent extracts and processes only the essential visual features needed for navigation (lane markings, road signs, traffic lights, intersections) from the complex environment, rather than storing and processing all mapping data. This selective extraction reduces data volume while maintaining navigation reliability by focusing on critical navigational elements.
Solution Approach 2:
The patent segments the visual environment into distinct navigational features (lane markings, signs, lights, intersections) that can be independently detected and processed. This segmentation allows the system to handle navigation data in manageable portions rather than as a monolithic data set, reducing overall data processing requirements.
2Adaptability or versatility
If the autonomous vehicle processes and interprets vast volumes of visual information and sensor data, then it can make appropriate navigation decisions, but the sheer quantity of data limits or adversely affects autonomous navigation
Solution Approach 1:
The patent applies different processing approaches to different types of visual information based on their local characteristics and navigational importance. Critical features like traffic lights and stop signs receive prioritized processing, while less critical elements are processed with lower computational resources, optimizing the balance between adaptability and complexity.
Solution Approach 2:
The system performs preliminary detection and classification of visual features before full navigation decision-making. By pre-identifying and categorizing navigational elements in advance, the system reduces the computational complexity required for final navigation decisions, as the heavy lifting of feature identification is already completed.
3Reliability
If the autonomous vehicle uses image analysis to detect open doors, then it can identify hazards and adjust navigation, but the system must process additional image data increasing computational load
Solution Approach 1:
The patent applies partial action by focusing computational resources only on detecting open doors in specific regions of interest rather than analyzing the entire image frame. This selective detection approach maintains safety detection capability while reducing overall computational energy consumption by processing only the necessary portions of visual data.
Data Source
AI summary
A computer-implemented method for navigating a host vehicle may include receiving an image frame acquired by an image capture device associated with the host vehicle; identifying in the image frame a representation of a target vehicle; determining an orientation indicator associated with the target vehicle; based on the determined orientation indicator, identifying a candidate region of the acquired image frame where a representation of a vehicle door of the target vehicle is expected in an open door condition; extracting the candidate region from the image frame; providing the candidate region to an open door detection network; determining a navigational action for the host vehicle in response to an indication from the open door detection network that the candidate region includes a representation of a door of the target vehicle in an open condition; and causing an actuator associated with the host vehicle to implement the navigational action.