3D Spatial Sensing Fusion for Wider Autonomous Vehicle Tracking
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Solution Overview
Problem
Current systems for autonomous vehicles, drones, and robots face challenges in reliably collecting and processing spatial information for stable operation, as existing sensor technologies often have limited sensing ranges and fail to effectively combine data from multiple sources for comprehensive 3D space tracking.
Innovation Solution
A vehicle and sensing device equipped with sensors like LiDAR, radar, and cameras, utilizing a neural network-based object classification model to acquire and track spatial information, which is then communicated to a server for reconstruction of a wider 3D space, enabling enhanced object identification and vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple sensors are used to expand sensing range, then the coverage area increases, but the device complexity increases
Solution Approach 1:
The patent combines data from multiple sensors (LiDAR, radar, cameras) into a unified spatial information representation. The server integrates sensor data from both moving vehicles and fixed sensing devices to reconstruct comprehensive 3D space maps, merging multiple data sources into a cohesive environmental model that expands sensing coverage while managing system complexity through centralized processing.
Solution Approach 2:
The patent transitions from 2D sensor data to 3D spatial reconstruction by integrating information from multiple sensors and perspectives. The server reconstructs three-dimensional space information by combining data from vehicles at different locations and fixed sensing devices, adding a dimensional aspect that expands the effective sensing coverage beyond what individual sensors can achieve.
2Speed
If spatial information is processed in real-time for autonomous driving, then the responsiveness improves, but the processing time and computational load increase
Solution Approach 1:
The server performs preliminary processing of spatial information by reconstructing 3D space maps and identifying objects in advance before the vehicle needs to make driving decisions. This pre-processing creates ready-to-use environmental models that can be quickly queried and applied during critical driving moments, reducing real-time processing requirements.
Solution Approach 2:
The server acts as an intermediary between raw sensor data and the vehicle's control system. It processes and reconstructs spatial information centrally, then provides processed results to the vehicle, separating the computationally intensive reconstruction tasks from the vehicle's real-time decision-making system and enabling faster local responses.
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 solution allows for real-time, accurate tracking and prediction of 3D spaces, improving the autonomy and safety of vehicles by integrating data from various sensors and fixed locations, thereby expanding sensing ranges and enhancing operational stability.
Implementation Method 1
a sensor unit configured to successively sense a three-dimensional (3D) space by using at least one sensor
Implementation Method 2
A vehicle and sensing device equipped with sensors like LiDAR, radar, and cameras
Implementation Method 3
A vehicle and sensing device equipped with sensors like LiDAR, radar, and cameras
Data Source
AI summary
A method of sensing a three-dimensional (3D) space using at least one sensor is proposed. The method can include acquiring spatial information over time for the sensed 3D space, applying a neural network based object classification model to the acquired spatial information over time to identify at least one object in the sensed 3D space. The method can also include tracking the sensed 3D space including the identified at least one object, and using information related to the tracked 3D space.