Adaptive Sensor Fusion via Reinforcement Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Vehicles equipped with multiple sensors of different modalities face challenges in accurately combining data under varying environmental conditions, leading to inconsistencies in resolution and accuracy, which affects vehicle operation in autonomous or semi-autonomous modes.
Innovation Solution
The method involves using reinforcement learning to train a deep neural network to determine the reliability of each vehicle data source, combining output data based on performance measures like collision probability and fuel consumption, and adjusting fusion weights to optimize sensor data fusion, ensuring accurate object location detection and vehicle path determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple sensors of different modalities are used to acquire environmental data, then the quantity and diversity of data increase, but the accuracy and reliability of combined data decrease due to inconsistencies under varying environmental conditions
Solution Approach 1:
The system dynamically changes the parameters of data fusion by adjusting fusion weights based on environmental conditions and sensor performance. The neural network learns optimal weight distributions for different sensor modalities (camera, lidar, radar) under various conditions, transforming the fusion process from static to adaptive parameter adjustment.
Solution Approach 2:
The patent implements a dynamic sensor fusion system where the fusion weights are not fixed but adapt in real-time based on environmental conditions and sensor reliability. The reinforcement learning agent continuously adjusts the weighting parameters, making the system dynamic rather than static, allowing optimal performance across varying operational contexts.
2Device complexity
If fixed fusion weights are used for combining sensor data, then the system complexity is reduced, but the adaptability to different environmental conditions deteriorates
Solution Approach 1:
The system employs a reinforcement learning agent that autonomously learns and adjusts fusion weights without requiring manual intervention or pre-programmed rules. The agent self-trains using reward signals based on detection accuracy, automatically adapting to different environmental conditions and sensor configurations, thereby achieving high adaptability while maintaining relatively simple system architecture.
Solution Approach 2:
The patent implements a feedback mechanism where the reinforcement learning agent receives reward signals based on the accuracy of object detection and localization. This feedback loop allows the system to learn from performance outcomes and continuously refine fusion weights, enabling adaptability to varying environmental conditions through iterative improvement rather than complex pre-programming.
3Speed
If sensor fusion is performed without considering sensor reliability, then the processing speed is maintained, but the collision risk increases due to inaccurate object detection
Solution Approach 1:
The system performs preliminary assessment of sensor reliability by evaluating detection outcomes and environmental conditions before finalizing fusion weights. The reinforcement learning agent proactively adjusts weights based on predicted sensor performance under current conditions, preventing inaccurate data from compromising detection accuracy and thereby reducing collision risk before it can manifest.
Data Source
AI summary
A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine performance of a plurality of vehicle data sources used to operate a vehicle by evaluating each vehicle data source output data and train a deep neural network to determine reliability for each of the vehicle data sources based on the performance using reinforcement learning. The instructions can further include instructions to combine output data from the vehicle data sources based on the reliability including using the deep neural network to correlate output data from one or more vehicle data sources to the performance to determine how accurately the output data from each vehicle data source corresponds to vehicle performance and operate the vehicle based on combined output data.