Attention-Driven Object Detection for Low-Compute Autonomous Driving
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Solution Overview
Problem
Autonomous driving technologies require significant computing power, leading to performance drops and potential safety risks due to reduced computing resources.
Innovation Solution
A method utilizing attention-driven resource allocation through a reinforcement learning algorithm and confidence scores to enhance object detection accuracy while minimizing computing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If computing power is reduced to save energy, then energy consumption decreases, but autonomous driving performance drops and safety is compromised
Solution Approach 1:
The patent segments the object detection process into two phases: an initial detection phase that processes the entire image, and a re-detection phase that selectively processes only unconfident regions identified by low confidence scores. This segmentation allows the system to reduce computing power consumption while maintaining detection accuracy in critical areas.
Solution Approach 2:
The patent applies local quality by directing enhanced computational resources (re-detection) only to specific regions with low confidence scores rather than uniformly processing the entire image. This ensures high reliability in uncertain regions while conserving energy in regions where the initial detection is already confident.
2Use of energy by moving object
If computing power is reduced, then energy consumption decreases, but object detection accuracy drops
Solution Approach 1:
The patent applies partial action by performing object detection only on specific unconfident regions rather than the entire image. The reinforcement learning agent selectively identifies regions requiring re-detection based on confidence scores, applying computational resources partially where needed rather than excessively across the whole image, thus reducing energy consumption while maintaining accuracy.
3Use of energy by moving object
If reinforcement learning is used to select regions for re-detection, then computing power is saved, but system complexity increases
Solution Approach 1:
The patent implements feedback through confidence scores that guide the reinforcement learning agent's region selection. The agent uses the confidence information from the initial detection as feedback to identify unconfident regions, creating a closed-loop system that adaptively allocates computing resources while managing complexity through information-driven decision making.
Data Source
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AI summary
A method for achieving better performance in an autonomous driving while saving computing powers, by using confidence scores representing a credibility of an object detection which is generated in parallel with an object detection process is provided. And the method includes steps of: (a) a computing device acquiring at least one circumstance image on surroundings of a subject vehicle, through at least one panorama view sensor installed on the subject vehicle; (b) the computing device instructing a Convolutional Neural Network(CNN) to apply at least one CNN operation to the circumstance image, to thereby generate initial object information and initial confidence information on the circumstance image; and (c) the computing device generating final object information on the circumstance image by referring to the initial object information and the initial confidence information, with a support of an RL agent.