Autonomous Vehicle Perception Fusion for Adverse-Weather Obstacle Detection
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Solution Overview
Problem
Current vehicle perception systems face challenges in accurately detecting obstacles in adverse weather conditions and rely heavily on training materials, limiting their effectiveness in various environments.
Innovation Solution
A method and apparatus utilizing a perception detection network that combines sensor data from cameras and radars to enhance obstacle detection, incorporating 4D reconstruction and spatiotemporal analysis, enabling improved perception and control of vehicle driving through voxel-based obstacle representation and integration of navigation and traffic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pure vision manner is used for obstacle detection, then the system can identify obstacles after training and learning, but it has high dependency on training materials and limited effectiveness in various environments
Solution Approach 1:
The patent combines multiple sensing modalities (vision sensors, radar, lidar) into a unified perception system. The vision system processes image data while radar/lidar provide depth and spatial information, creating a multi-sensor fusion architecture that overcomes the limitations of pure vision systems and improves both accuracy and environmental adaptability
2Measurement precision
If lidar or millimeter-wave radar is used for obstacle detection, then the system can detect obstacles in surroundings, but detection accuracy is low in rainy or snowy weather
Solution Approach 1:
The patent creates a composite sensing system that integrates different sensor types (vision cameras, radar, lidar) with complementary characteristics. The vision system is less affected by weather compared to radar/lidar, and by fusing data from all sensors, the system compensates for individual sensor weaknesses in adverse weather conditions
3Reliability
If traditional perception systems are used, then the system can detect obstacles, but collision avoidance capability is limited
Solution Approach 1:
The patent introduces voxel-based 3D spatial representation and temporal dimension through 4D reconstruction (3D space + time). This multi-dimensional approach enables more accurate obstacle localization, trajectory prediction, and collision risk assessment, significantly improving collision avoidance capability while managing system complexity through structured data representation
Data Source
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AI summary
An intelligent driving method and an apparatus are disclosed. The method includes: obtaining collected data of a sensor of a vehicle for a scene, where the sensor includes at least one of a camera and a radar; inputting the collected data into a perception detection network, and outputting perception information, where the perception information indicates a voxel of an obstacle in a first scene; and controlling driving of the vehicle based on at least the perception information. This can enhance a perception capability of the vehicle for surroundings, help improve obstacle detection accuracy, and avoid a collision.