Automated Driving Control Device Using High-Precision Lane Maps
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Solution Overview
Problem
Conventional coordinate systems, such as ECEF and TM, fail to accurately identify a vehicle's lane and position on the road, leading to potential accidents in autonomous driving systems.
Innovation Solution
An automated driving control device and system that utilizes a high-precision lane-level road map for position recognition, fusing position and vehicle control information, and obstacle detection to generate a path for safe navigation, employing a position recognition controller and vehicle controller for real-time processing via CAN communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional coordinate systems (ECEF, TM) are used for position recognition, then the system structure is simple, but the position recognition precision is insufficient to accurately identify vehicle lane and position
Solution Approach 1:
The position recognition system is segmented into multiple functional modules: coordinate transformation unit, lane identification unit, vehicle position calculation unit, and obstacle detection unit. Each module performs a specific function in the position recognition process, enabling accurate lane-level positioning while maintaining clear system architecture and manageability
Solution Approach 2:
A high-precision lane-level road map is introduced as an intermediary data structure between the coordinate system and the vehicle position recognition. This road map contains detailed lane geometry information that serves as a reference for accurately determining vehicle position and lane, bridging the gap between simple coordinate systems and precise position recognition requirements
2Measurement precision
If detailed lane information and high-precision road maps are processed in real-time, then position recognition precision improves, but communication load and processing time increase
Solution Approach 1:
The high-precision lane-level road map is preprocessed and stored in advance, containing all necessary lane geometry information, connection relationships, and spatial data. This preliminary preparation eliminates the need for real-time complex calculations, allowing the system to quickly query and match vehicle position against pre-stored lane data, achieving both high precision and real-time performance
Solution Approach 2:
Only the essential lane-level position information and minimal necessary road map data are extracted and transmitted through CAN communication, rather than processing complete high-precision maps. This selective extraction reduces communication load while maintaining sufficient position recognition precision for automated driving control
3Loss of information
If complete position recognition information and vehicle control information are transmitted through CAN communication, then information completeness is high, but communication load increases
Solution Approach 1:
The system extracts and transmits only the critical position recognition parameters (vehicle position, lane ID, heading angle) and essential vehicle control information through CAN communication. Non-essential detailed data is processed locally or pre-stored, minimizing communication bandwidth requirements while maintaining sufficient information completeness for safe automated driving control
Solution Approach 2:
Different levels of information completeness are applied to different communication scenarios and processing stages. Full precision data is used for critical safety decisions, while reduced precision data suffices for routine position updates, optimizing the balance between information completeness and communication efficiency across different operational contexts
Data Source
AI summary
The present disclosure relates to an automated driving control device, a system including the automated driving control device, and a method of automated driving control. The automated driving control device may include: a high-precision lane-level road map storage storing a high-precision lane-level road map; a position recognition controller recognizing a current position of a vehicle based on the high-precision lane-level road map, position recognition information, and vehicle control information; and a vehicle controller generating a path for driving to a destination based on the position of the vehicle recognized by the position recognition controller and obstacle recognition information, and controlling driving of the vehicle.