Vehicle Environmental Data Fusion for Adverse-Weather Obstacle Detection
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Solution Overview
Problem
Autonomous driving systems face challenges in accurately processing environmental data, particularly in adverse weather conditions, as traditional depth sensing devices often provide inaccurate distance readings due to poor weather, increasing the risk of collisions.
Innovation Solution
The method involves receiving depth data from sensors like radar or lidar and thermal data from thermographic cameras, fusing these to generate fused environmental data, which is then used to create an augmented occupancy grid and provided to a neural network for improved driving scene classification and path information generation, enabling the vehicle to distinguish between cold and hot objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional depth sensors (radar, lidar) are used for autonomous driving, then the system can obtain distance information, but the measurement precision deteriorates in adverse weather conditions such as heavy rain or fog
Solution Approach 1:
The patent combines depth sensors (radar or lidar) with thermal sensors to create a fused sensing system. The depth sensor provides distance information while the thermal sensor detects temperature signatures, and their data is merged through fusion algorithms to produce more reliable object detection and classification, especially in adverse weather conditions where one sensor type may struggle.
Solution Approach 2:
The thermal sensor acts as an intermediary that provides complementary information about objects detected by the depth sensor. By detecting thermal signatures, the thermal sensor can identify living vs. non-living objects and provide verification or correction to depth measurements, especially when weather conditions degrade radar or lidar performance.
2Reliability
If multiple sensor types (depth sensor and thermal sensor) are fused, then the reliability of environmental data processing is improved, but the device complexity increases
Solution Approach 1:
The system segments the sensing function into two distinct modules: depth sensing (for distance and spatial information) and thermal sensing (for temperature and object classification). This segmentation allows each sensor type to be optimized independently while their data is integrated through fusion algorithms, managing complexity by dividing the overall sensing task.
Solution Approach 2:
The fused sensor system provides multiple functions: distance measurement, object detection, object classification (living vs. non-living), and environmental monitoring. By creating a multi-functional system that combines depth and thermal sensing, the patent achieves universal applicability for various autonomous driving scenarios while managing complexity through integrated processing.
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 enhances the accuracy of driving scene classification and reduces the risk of fatalities by providing reliable information for emergency brake maneuvers, even in adverse weather conditions, by effectively differentiating between living and non-living obstacles.
Implementation Method 1
receiving thermal data of the environment of the vehicle from at least one thermal sensor of the vehicle
Implementation Method 2
The depth sensor may, for example, be a radar sensor
Implementation Method 3
The depth sensor may, for example, be a radar sensor, a lidar sensor
Data Source
AI summary
A method, a computer program code, an apparatus for processing environmental data of an environment of a vehicle, a driver assistance system, which makes use of such a method or apparatus, and an autonomous or semi-autonomous vehicle comprising such a driver assistance system. Depth data of the environment of the vehicle is received from at least one depth sensor of the vehicle. Furthermore, thermal data of the environment of the vehicle is received from at least one thermal sensor of the vehicle. The depth data and the thermal data are then fused to generate fused environmental data.