Autonomous Driving Sensor Fusion for Adaptive Camera Power Allocation
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Solution Overview
Problem
Autonomous driving systems consume excessive electricity due to unnecessary operation of cameras when no obstacles are present, and existing reinforcement learning techniques face challenges in defining ground truth, leading to inefficient resource allocation and potential risks during actual driving.
Innovation Solution
A method utilizing reinforcement learning with a computing device that integrates attention, detection, and drive networks to optimize camera usage based on sensor data from radar and LiDAR, adjusting parameters through virtual space simulations to minimize power consumption and reduce risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all cameras are operated continuously to ensure comprehensive monitoring, then detection coverage is improved, but power consumption increases significantly
Solution Approach 1:
The system dynamically adjusts camera operation states based on real-time sensor data and calculated attention scores. Cameras are selectively activated or deactivated depending on the detected driving situation, transforming the static all-or-nothing camera operation into a dynamic adaptive system that optimizes power consumption while maintaining necessary detection coverage.
Solution Approach 2:
The autonomous driving system autonomously decides which cameras to operate based on its own sensor data processing and attention calculation, without requiring external control. The system serves itself by automatically optimizing its own resource allocation, determining camera activation states based on the current driving context and attention scores.
2Measurement precision
If reinforcement learning is performed in actual driving to optimize resource allocation, then learning accuracy is improved, but safety risks increase due to potential car crashes
Solution Approach 1:
The system creates a virtual copy of the driving environment where reinforcement learning can be performed safely. Instead of learning directly in the real world with physical vehicles, the learning process occurs in a simulated virtual space that replicates driving scenarios, allowing the system to gain learning experience without exposing physical vehicles to safety risks.
Solution Approach 2:
The system performs preliminary learning actions in the virtual space before applying learned policies to actual driving. By pre-training the reinforcement learning agent in a safe virtual environment, the system accumulates learning experience and optimizes resource allocation strategies beforehand, reducing the need for risky trial-and-error learning during actual driving operations.
3Speed
If Convolutional Neural Network is used for resource allocation, then processing speed is improved, but learning capability deteriorates due to inability to define ground truth
Solution Approach 1:
The system replaces the conventional Convolutional Neural Network approach with reinforcement learning. Instead of relying on supervised learning with predefined ground truth labels, the system uses reinforcement learning that learns through trial-and-error interactions with the environment, using reward signals to guide learning without requiring explicit ground truth definitions.
Data Source
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AI summary
A method for efficient resource allocation in autonomous driving by reinforcement learning is provided for reducing computation via a heterogeneous sensor fusion. This attention-based method includes steps of: a computing device instructing an attention network (130) to perform a neural network operation by referring to attention sensor data, to calculate attention scores; instructing a detection network (140) to acquire video data by referring to the attention scores and to generate decision data for the autonomous driving; instructing a drive network (150) to operate the autonomous vehicle by referring to the decision data, to acquire circumstance data, and to generate a reward by referring to the circumstance data; and instructing the attention network (130) to adjust parameters used for the neural network operation by referring to the reward. Thus, a virtual space where the autonomous vehicle optimizes the resource allocation can be provided by the method.