Stereo Camera and LIDAR Fusion for Road User Orientation
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Solution Overview
Problem
Existing systems for autonomous vehicles require significant computational resources to evaluate LIDAR and image data, which can lead to increased power consumption and delays in determining the orientation of other road users.
Innovation Solution
A method that dynamically evaluates either image data from a stereo camera system or both image and LIDAR data based on a predetermined quality measure, using machine learning algorithms and data fusion techniques to reduce computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR data and image data are both evaluated to determine orientation with high quality, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system dynamically adapts the evaluation method based on real-time quality requirements. When high orientation determination quality is needed, both LIDAR and image data are evaluated. When lower quality suffices, only image data is evaluated, reducing computational load and power consumption accordingly.
Solution Approach 2:
The system changes the processing parameters by selecting different data evaluation modes (full dual-sensor evaluation vs. image-only evaluation) based on the required quality level, thereby adjusting the balance between measurement precision and energy consumption.
2Measurement precision
If LIDAR data and image data are both evaluated to determine orientation with high quality, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system dynamically selects the evaluation approach based on quality requirements. For time-critical applications where lower quality suffices, only image data is processed, enabling faster response. When high precision is required and time permits, both LIDAR and image data are evaluated for more accurate orientation determination.
Solution Approach 2:
The system performs partial evaluation (image data only) when full evaluation (both LIDAR and image data) is not necessary, achieving sufficient orientation determination quality with reduced processing time and computational resources.
3Use of energy by moving object
If only image data is evaluated, then use of energy is reduced, but measurement precision deteriorates
Solution Approach 1:
The system adjusts the processing parameters by selecting image-only evaluation mode when energy consumption needs to be minimized and high orientation determination quality is not critical, thereby reducing power consumption at the cost of some precision.
4Use of energy by moving object
If computational resources are reduced, then use of energy is reduced, but measurement precision deteriorates
Solution Approach 1:
The system dynamically adjusts computational resource allocation based on quality requirements. When lower computational resources are available or energy is constrained, the system uses image data only. When higher computational resources are available and high precision is needed, both LIDAR and image data are processed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for accurate determination of the orientation of road users while minimizing the computational load, thereby reducing power consumption and improving response times in autonomous vehicle systems.
Implementation Method 1
receiving and evaluating image data to determine an orientation of a road user
Implementation Method 2
having a LIDAR sensor for capturing LIDAR data
Implementation Method 3
LIDAR (acronym for light detection and ranging) in this case denotes a method, related to radar, for optically measuring distance and speed and for range-finding. Rather than using radio waves as in RADAR, laser beams are used.
Data Source
AI summary
A request signal that indicates a quality for a determination of an orientation of a road user is received. The orientation of the road user is determined based on a) image data when the request signal indicates the quality is below a predetermined quality for the determination of the orientation of the road user, or b) on LIDAR data and image data when the request signal indicates the quality is the predetermined quality.