Vehicle Path Determination Using Sensor-Map Trajectory Comparison
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Solution Overview
Problem
Existing methods for determining a vehicle's path are often inaccurate due to outdated GPS maps and require costly infrastructure, while current solutions are complex and expensive, especially in areas with limited landmark visibility or radar coverage.
Innovation Solution
A method that compares a vehicle's sensor-derived trajectory with a digital map trajectory to identify differences, allowing for a corrected path determination and map updating using low-cost sensors and minimal computational resources, which can be applied to improve map quality through crowd-sourced data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS differential corrections are used to improve positioning accuracy, then measurement precision is improved, but device complexity increases due to requiring base stations infrastructure
Solution Approach 1:
The system uses the vehicle's own sensors (odometry, IMU) to track its position and compare with map data, making the vehicle self-sufficient for positioning without external base stations. The vehicle serves itself by using its motion sensors to determine trajectory and validate against digital map geometry.
Solution Approach 2:
The digital map acts as an intermediary reference framework. Instead of using complex base station infrastructure, the system mediates positioning by comparing sensor-derived trajectories with the known geometry of roads and pathways in the digital map, using the map as a reference intermediary.
2Measurement precision
If radar/vision maps with landmark matching are used to improve positioning accuracy, then measurement precision is improved, but device complexity increases due to requiring vision equipment and high quality maps
Solution Approach 1:
The system replaces vision-based mechanical/optical systems with inertial sensing and computational methods. Instead of using cameras and vision algorithms to detect landmarks, the system uses IMU and odometry sensors combined with digital map geometry to determine position, substituting mechanical vision systems with sensor fusion and computational trajectory matching.
Solution Approach 2:
The system uses inexpensive sensor data (odometry, IMU) that is readily available in most vehicles, replacing the need for expensive high-quality maps and vision equipment. The approach uses commodity sensors and standard digital maps rather than specialized expensive infrastructure.
3Reliability
If sensor aggregation and fusion are used to improve positioning reliability, then reliability is improved, but device complexity increases due to requiring multiple sensors and complex fusion algorithms
Solution Approach 1:
The system makes existing vehicle sensors multi-functional by using odometry and IMU data for both their primary navigation functions and for trajectory determination in the positioning system. This universal use of existing sensors improves reliability without adding specialized equipment or complex fusion infrastructure.
Solution Approach 2:
The system uses the vehicle's existing sensor suite to serve dual purposes: primary vehicle control/navigation and secondary trajectory determination for map matching. The vehicle's own sensors self-serve the positioning function without requiring external sensor aggregation or complex fusion systems.
4Ease of operation
If outdated digital maps are used for path determination, then ease of operation is maintained, but measurement precision deteriorates due to outdated map information
Solution Approach 1:
The system performs preliminary trajectory determination using sensor data before comparing with the digital map. By pre-calculating the sensor-derived trajectory and then matching it with map geometry, the system prepares the data in advance for accurate comparison, improving precision while maintaining simplicity.
Solution Approach 2:
The system uses feedback from the comparison between sensor trajectories and digital map trajectories to identify and correct discrepancies. The feedback loop detects differences and uses them to improve positioning accuracy, allowing the system to work effectively even with outdated maps by continuously refining position estimates.
Data Source
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Figure 3a~3b
AI summary
The invention relates to a method for determining a path of a vehicle comprising the steps: determining a first trajectory of the vehicle over a time interval based on information extracted from a digital map, determining, over said time interval, a second trajectory of the vehicle based on information extracted from at least one vehicle sensor, comparing the first and the second trajectories to identify differences between the information extracted from the digital map and the at least one vehicle sensor, and determining a corrected path of the vehicle based on the identified differences. The invention relates furthermore to method for updating information contained in a digital map and system for determining a path of a vehicle.