Vehicle Occupant Behavior Estimation Using 3D Sensor Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autonomous vehicle systems struggle to accurately and robustly estimate three-dimensional occupant behaviors within the vehicle, which is crucial for safety, especially in autonomous driving environments where human intervention is minimal.
Innovation Solution
An occupant behavior estimation system utilizing a vehicle interior camera and sensors, combined with a particle filter, to process images and sensor data for robust three-dimensional behavior estimation, including key point extraction and object tracking, and a vehicle safety controller to fuse this information for precise behavior prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a particle filter is used to fuse camera and sensor information for robust occupant behavior estimation, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The system segments the complex estimation problem into distinct modules: camera-based key point detection, sensor-based vehicle state monitoring, and particle filter-based fusion processing. Each module handles specific aspects of occupant behavior estimation independently, then integrates results through the particle filter framework, reducing overall system complexity while maintaining precision.
Solution Approach 2:
The particle filter acts as an intermediary that mediates between camera observations and sensor data. It fuses these heterogeneous information sources through probabilistic modeling, producing robust occupant behavior estimates without requiring direct complex integration of all sensor inputs, thus simplifying the system architecture while improving measurement precision.
2Reliability
If multiple sensors and cameras are integrated for continuous occupant monitoring, then reliability and safety are improved, but device complexity and cost increase
Solution Approach 1:
The system merges camera-based visual monitoring with sensor-based physical monitoring into a unified occupant behavior estimation framework. The particle filter combines key point information from cameras with vehicle state information from sensors, creating a redundant but complementary monitoring system that improves reliability while managing complexity through integrated processing.
Solution Approach 2:
The particle filter framework serves multiple functions simultaneously: it fuses heterogeneous data from different sensors, tracks occupant behavior over time, handles missing or failed sensor inputs, and provides probabilistic confidence measures. This multi-functionality improves monitoring reliability without requiring separate dedicated systems for each function, thereby controlling overall system complexity.
Data Source
AI summary
To robustly estimate three-dimensional behaviors of an occupant by fusing, through a particle filter, information obtained through vehicle indoor cameras and through vehicle internal information sensors, an occupant behavior estimation system includes: a camera configured to obtain images of the at least one occupant within the vehicle; sensors configured to obtain information on the vehicle; an image processing device configured to process images obtained from the camera and to obtain key point information of the at least one occupant and object tracking information that is provided by tracking the at least one occupant; and a vehicle safety controller configured to estimate the behaviors of the occupant by using a particle filter based on the information on the vehicle obtained through the sensors, the key point information and the object tracking information, which are obtained from the image processing device.