Dynamic Map Generation via Multi-Vehicle LiDAR Data Integration
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Solution Overview
Problem
Self-driving vehicles equipped with LiDAR sensors face challenges in detecting multiple adjacent obstacles due to blind zones, where one obstacle obstructs the view of another, leading to incomplete geometric information and difficulty in determining if an object is an obstacle.
Innovation Solution
A system comprising vehicle devices with LiDAR sensors and cameras, relay hosts, and a cloud server that integrates point cloud data from multiple vehicles to generate dynamic map information, expanding the sensing area by aligning and merging 3D coordinates data with a base map, and transmitting this information to each vehicle for enhanced obstacle detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If LiDAR sensor is used to sense environment and generate point cloud data, then 3D geometric information of surrounding environment is obtained quickly, but blind zones are created when multiple obstacles are adjacent to each other causing incomplete geometric information
Solution Approach 1:
The patent merges point cloud data from multiple vehicles through relay hosts to a cloud server, combining sensing results from different viewing angles and positions to eliminate blind zones and obtain complete geometric information of adjacent obstacles
Solution Approach 2:
Relay hosts act as intermediaries that receive point cloud data from multiple vehicles, perform data integration, and transmit processed information to the cloud server, enabling cooperative sensing without direct vehicle-to-vehicle communication
2Area of stationary object
If multiple vehicles send point cloud data to cloud server for integration, then sensing area is expanded and blind zones are mitigated, but communication data transmission requirements increase
Solution Approach 1:
The patent extracts only essential 3D coordinates information from point cloud data after local processing at relay hosts, transmitting only necessary information to the cloud server rather than raw point cloud data, reducing communication burden while maintaining sensing area expansion benefits
Solution Approach 2:
The system segments the data processing task into local preprocessing at relay hosts and centralized integration at cloud server, with each relay host handling its own received data independently before aggregation, enabling scalable system expansion
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 effectively mitigates blind zones by expanding the sensing area of each vehicle, enabling more accurate obstacle detection and safer route planning for self-driving vehicles through high-definition map integration and real-time environment information sharing.
Implementation Method 1
Light detection and ranging (LiDAR) device is widely used in self-driving systems. It can quickly sense the surrounding environment and generate point cloud data representing the surrounding environment.
Data Source
AI summary
A method and a system for generating dynamic map information with environment information are provided, wherein the system includes a cloud server, multiple relay hosts distributed around the environment and multiple vehicle devices each installed respectively in different vehicles. Each vehicle device includes a LiDAR sensor and a camera for sensing the environment to respectively generate point cloud data and image data. When the point cloud data from different vehicles are transmitted to a neighboring relay host, the relay host performs a multi-vehicle data integration mode to merge the point cloud data and obtain 3D coordinates information of objects in the environment according to the merged data. Based on the 3D coordinates information of objects, the cloud server generates and transmits dynamic map information to the vehicles. By sharing sensing data of different vehicles, the sensing area of each vehicle is expanded to mitigate dark zones or blind zones.