Active Driving Map for Autonomous Vehicle Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Self-driving road vehicles face challenges in accurately navigating real-world roadways due to outdated or incomplete roadway models, which can lead to inefficiencies in route planning and collision avoidance.
Innovation Solution
The implementation of an active driving map (ADM) system that provides a dynamically updated data store of route feature data, including sequential series of road cross-section data objects, to the control system of self-driving vehicles, enabling them to accurately represent and navigate real-world roadways by integrating sensor data with a server process that receives position, orientation, and motion measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If roadway models are updated frequently to improve navigation accuracy, then measurement precision is improved, but use of energy increases due to continuous data processing and communication
Solution Approach 1:
The system dynamically updates roadway model data based on vehicle position and navigation needs, rather than continuously updating all data. The active driving map selectively loads and updates only the relevant roadway segments that the vehicle will encounter, optimizing the balance between accuracy and energy consumption.
Solution Approach 2:
The system pre-loads roadway model data for upcoming navigation segments before the vehicle reaches them. This allows the vehicle to have accurate roadway information available in advance without requiring continuous real-time updates, reducing the energy burden of last-minute data processing.
2Reliability
If complete roadway data is stored to improve navigation reliability, then reliability is improved, but device complexity increases due to large data storage requirements
Solution Approach 1:
The roadway model data is segmented into discrete roadway segments that can be independently loaded and managed. The active driving map maintains only the segments relevant to the current and upcoming navigation routes, rather than storing complete roadway network data, thus reducing storage complexity while maintaining navigation reliability.
Solution Approach 2:
The roadway model data structure is designed to be multi-functional, serving both detailed navigation planning and high-level route guidance. This universal data structure allows the same data to support multiple navigation functions, reducing the need for separate data stores and thereby reducing overall system complexity.
Data Source
AI summary
A self-driving road vehicle stores an active driving map that includes a data store of route feature data together with a software or control system that periodically selects from the data store and provides to systems on the vehicle the route feature data that are relevant to the vehicle location and the route that the vehicle is following. The route feature data may include a sequential series of road cross-section data objects that represent a real-world roadway being traversed by the vehicle. Methods for operating the self-driving road vehicle include providing route feature data from an active driving map, which may include the sequential series of road cross-section data objects that represent the real-world roadway being traversed.