Deep Learning Beam Control for Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles face challenges in automatically controlling light emission intensities for headlamps, requiring manual annotation of training data and additional sensors, which limits their ability to adapt to real-time environmental conditions effectively.
Innovation Solution
A deep learning-based beam control system that uses a convolutional neural network to classify beam illumination intensity based on fused image and object features, eliminating the need for manual annotation and additional sensors by reusing existing sensor data for autonomous vehicle operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual annotation of training data is used for beam control, then the system can be trained to control light emission intensities, but the complexity of data preparation and system configuration increases
Solution Approach 1:
The system uses existing sensor data from autonomous vehicle operations to automatically train the beam control model without requiring manual annotation. The model learns from real-world sensor data that is already being collected for other autonomous driving functions, making the training process self-service and eliminating manual intervention.
Solution Approach 2:
The patent reuses existing sensor data (cameras, LIDAR, radar) that are already deployed for autonomous vehicle perception and navigation. This same multi-functional sensor data is applied to train the beam control model, eliminating the need for dedicated additional sensors or specialized data collection systems.
2Reliability
If additional sensors are deployed for beam control data collection, then more comprehensive training data can be obtained, but the device complexity and cost increase
Solution Approach 1:
The system achieves reliable beam control training by repurposing sensor data already collected for autonomous vehicle operations. Cameras, LIDAR, and radar data used for navigation and obstacle detection are simultaneously used to train the beam control model, eliminating the need for additional dedicated sensors.
Solution Approach 2:
The existing sensor infrastructure serves dual purposes: autonomous vehicle navigation and beam control training. The system self-serves by utilizing its own operational sensor data for model training without requiring external or additional sensing resources.
3Adaptability or versatility
If the beam control system is updated with new data, then the model can adapt to new environments and conditions, but the process time and operational interruption increase
Solution Approach 1:
The system implements continuous feedback loops where sensor data from autonomous vehicle operations is continuously collected and used to iteratively retrain and update the beam control model. This feedback mechanism enables the model to adapt to new environments and conditions while the vehicle is in operation, minimizing downtime.
Solution Approach 2:
The beam control model training and updating process occurs continuously during normal vehicle operations rather than requiring separate dedicated training periods. The system maintains continuous useful action by learning from ongoing sensor data collection without interrupting service.
Data Source
AI summary
Provided are systems and methods for a deep learning based beam control. Sensor data associated with the environment and the corresponding detected objects from a perception system are obtained. Object features and image features are extracted. The extracted object features and image features are fused into fused features. A beam control status is predicted according to the fused features, wherein the beam control status indicates a high beam illumination intensity or a low beam illumination intensity of a light emitting device.