Autonomous Vehicle Occlusion Detection With Confidence-Based Response
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and navigating around occlusions, such as hills or structures, which can obstruct the view of the road and objects, leading to unstable or unsafe operations.
Innovation Solution
The system employs cameras and map information to identify occlusions, determine confidence scores based on object proximity to occlusions, and adjust operation algorithms accordingly, including deceleration or switching object detection models to ensure safe navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the autonomous vehicle operates at high speed, then productivity is improved, but safety deteriorates when occlusions are present
Solution Approach 1:
The system performs preliminary detection of occlusions using map information and camera parameters before the vehicle reaches high-speed operation zones. By identifying occlusions in advance and calculating confidence scores, the system can prepare appropriate response algorithms, allowing high-speed operation when safe and automatic deceleration when occlusions are detected, thus resolving the contradiction between productivity and safety
2Stability of the object's composition
If the vehicle maintains steady operation, then stability is improved, but responsiveness to occlusions deteriorates
Solution Approach 1:
The system dynamically adjusts operation algorithms based on real-time confidence scores calculated from occlusion detection. When occlusions are detected with high confidence, the system automatically switches to deceleration algorithms or alternative detection models. This dynamic adaptation allows the vehicle to maintain stability during normal operation while responding rapidly when occlusions are identified, resolving the contradiction between stability and responsiveness
3Device complexity
If basic object detection is used, then device complexity is reduced, but measurement precision deteriorates in occluded conditions
Solution Approach 1:
The system introduces map information and camera parameter data as intermediary elements that help identify occlusions without requiring complex additional sensors. By using these intermediaries to calculate confidence scores and determine whether occlusions are present, the system can switch between basic and enhanced detection modes, maintaining measurement precision in occluded conditions while avoiding unnecessary complexity in clear conditions
Data Source
AI summary
The present disclosure provides methods and systems for operating an autonomous vehicle. In some embodiments, the system may obtain, by a camera associated with an autonomous vehicle, an image of an environment of the autonomous vehicle, the environment including a road on which the autonomous vehicle is operating and an occlusion on the road. The system may identify the occlusion in the image based on map information of the environment and at least one camera parameter of the camera for obtaining the image. The system may identify an object represented in the image, and determine a confidence score relating to the object. The confidence score may indicate a likelihood a representation of the object in the image is impacted by the occlusion. The system may determine an operation algorithm based on the confidence score; and cause the autonomous vehicle to operate based on the operation algorithm.