In-Cabin Camera Localization for Vehicle Seating Zone Detection
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Solution Overview
Problem
Current vehicle cabin localization systems, such as those using RADAR, are unsuitable for tasks like face recognition and eye gaze detection, and monocular cameras with 3D models are needed for accurate object localization within vehicle seating zones.
Innovation Solution
A system utilizing 2D image sensors and a machine-learning model trained on images from specific vantage points within the vehicle to identify and localize objects within defined seating zones using bounding boxes, allowing for accurate 3D placement of detected persons and objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RADAR is used for object detection in vehicle cabin, then detection range and penetration capability are improved, but suitability for face recognition and eye gaze detection deteriorates
Solution Approach 1:
The system segments the detection task by using multiple sensor types (cameras, RADAR, LIDAR) for different functions. Cameras are specifically used for face recognition and eye gaze detection where high precision is needed, while RADAR handles broader detection tasks, dividing the overall detection problem into specialized sub-tasks.
Solution Approach 2:
The system introduces an intermediary processing layer that fuses data from multiple sensors. The camera images provide detailed visual information for face and eye detection, while RADAR provides depth and motion data, with the fusion algorithm combining these intermediate results to achieve comprehensive detection capabilities that neither sensor could achieve alone.
2Measurement precision
If monocular camera with 3D model is used for object localization, then accuracy in seating zone localization is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models with synthetic 3D camera images generated from vehicle cabin models. This pre-training prepares the system in advance for specific seating zone localization tasks, reducing the complexity of real-time processing by having the model already adapted to the expected scenarios.
Solution Approach 2:
The system creates virtual copies of the vehicle cabin environment through 3D modeling and synthetic image generation. These digital twins replicate the physical cabin geometry and lighting conditions, allowing the machine learning model to be trained on unlimited synthetic data without requiring extensive real-world data collection and annotation.
3Productivity
If machine learning model is trained on synthetic 3D camera images, then training efficiency and adaptability to specific vehicle models are improved, but reliance on accurate 3D models is increased
Solution Approach 1:
The system performs preliminary action by generating and storing synthetic training images for various vehicle configurations before deployment. This advance preparation allows rapid adaptation to different vehicle models simply by generating new synthetic images from updated 3D models, rather than requiring complete retraining from scratch.
Solution Approach 2:
The system exploits parameter changes by varying lighting conditions, camera positions, and object placements in the synthetic 3D scene generation. This creates diverse training data that robustifies the model against variations in real-world conditions, reducing dependence on any single 3D model's accuracy.
Data Source
AI summary
Performing object localization inside a cabin of a vehicle is provided. A camera image is received of a cabin of a vehicle by an image sensor at a first location with respect to a plurality of seating zones of a vehicle. Object detection on the camera image is performed to identify one or more objects in the camera image. A machine-learning model trained on images taken at the first location is utilized to place the one or more objects into the seating zones of the vehicle according to a plurality of bounding boxes corresponding to the plurality of seating zones for the first location.