Vehicle Map Matching With GNSS and Odometry for New Road Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining whether a motor vehicle has driven on a road included in digital map material are inadequate when the distance between new, unknown roads and known roads is short, as they cannot reliably identify new roads in close proximity, posing safety risks for autonomous vehicles.
Innovation Solution
A method that captures absolute position data and vehicle odometry data at multiple points in time, using a combination of these data with a map-matching algorithm to determine if a vehicle has driven on a road included in digital map material, allowing for accurate identification of new roads by comparing route lengths and actual distances covered.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only absolute position data from GNSS is used for road identification, then the system is simple to operate, but new roads cannot be reliably identified when they are close to known roads (distance < 100m)
Solution Approach 1:
The patent combines absolute position data from GNSS with vehicle odometry data (wheel speed sensor, steering angle sensor, yaw rate sensor) to create a hybrid positioning system. This merging of multiple data sources enables reliable identification of new roads even when they are less than 100m from known roads, overcoming the limitation of using GNSS alone while maintaining practical system complexity.
Solution Approach 2:
The patent introduces map-matching algorithms as an intermediary process that compares the combined positioning data with digital map material. This intermediary step reconciles the raw sensor data with known road networks, enabling the system to identify deviations that indicate new roads without requiring direct complex processing of all raw sensor inputs.
2Reliability
If GNSS position data is mapped to digital map using map-matching methods, then the system can identify roads at great distance (>100m), but it cannot reliably identify new roads when distance to known roads is short
Solution Approach 1:
The patent changes the parameters used for position determination by incorporating vehicle odometry data (wheel speed, steering angle, yaw rate) alongside GNSS data. This parameter expansion allows the system to maintain position determination accuracy while improving reliability for identifying new roads in close proximity to known roads, where GNSS alone would be insufficient.
Solution Approach 2:
The patent performs preliminary processing of vehicle odometry data through integration and filtering before combining it with GNSS data. This preliminary action prepares the odometry data in a form that can be effectively combined with map-matching algorithms, enhancing the system's ability to reliably identify new roads without compromising position determination accuracy.
3Reliability
If digital map material is continuously updated, then the system remains accurate for navigation, but new roads are not captured quickly enough to prevent safety risks
Solution Approach 1:
The patent enables the system to self-identify new roads by detecting deviations between the vehicle's actual path (from combined GNSS and odometry data) and the known road network (from digital map material). This self-service capability allows immediate detection of new roads without waiting for external map updates, preventing safety risks while autonomous vehicles are the first to use these roads.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously compares expected vehicle positions (based on digital map roads) with actual vehicle positions (from combined positioning data). When deviations exceed thresholds, the system identifies potential new roads and can trigger updates to digital map material, creating a rapid feedback loop that reduces the time lag between new road construction and map updates.
Data Source
AI summary
Methods, systems, and electronic control units are provided. It is determined whether a motor vehicle has driven on a road included in digital map material. First absolute position data is captured relating to the motor vehicle using an absolute positioning system and capturing first vehicle odometry data using an odometry system of the motor vehicle at a first point in time during operation of the motor vehicle. Further absolute position data is captured relating to the motor vehicle using the absolute positioning system and capturing further vehicle odometry data using the odometry system at one further point in time during operation of the motor vehicle which differs from the first point in time. It is determined whether the motor vehicle has driven on a road included in digital map material based on the digital map material, the captured data, and a map-matching algorithm.