Vehicle Localization Using Static Objects When GNSS Signals Fail
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Solution Overview
Problem
Existing vehicle navigation systems relying on satellite signals face challenges when signal strength is weak or lost, leading to inaccurate localization and inability to operate autonomously in areas with poor satellite coverage.
Innovation Solution
Utilizing a combination of on-board sensors, such as cameras, radars, and wheel speed sensors, to determine vehicle location and heading angle, fusing data with static object detection to maintain accurate vehicle control during satellite signal loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If satellite signal is used for vehicle localization, then localization accuracy is improved, but system reliability deteriorates when satellite signal is weak or lost
Solution Approach 1:
The patent combines satellite-based localization with sensor-based localization (using cameras, radars, and wheel speed sensors) to create a hybrid system. When satellite signals are weak or lost, the system seamlessly transitions to using sensor data and static object detection to maintain localization accuracy and system reliability simultaneously.
Solution Approach 2:
The system dynamically changes localization parameters by switching between satellite signal processing and sensor data processing based on signal strength conditions. When satellite signal strength falls below a threshold, the system changes its operational mode to rely on alternative parameters such as static object positions and vehicle motion data.
2Reliability
If sensor-based localization is used when satellite signal is lost, then system reliability is improved, but measurement precision deteriorates
Solution Approach 1:
Static objects serve as intermediary reference points between the vehicle's sensor system and the global coordinate system. By detecting static objects (trees, buildings, poles) and matching them with pre-stored map data, the system maintains centimeter-level localization accuracy even when satellite signals are unavailable.
Solution Approach 2:
The system performs preliminary actions by pre-storing high-definition map data including positions of static objects before satellite signal loss occurs. This allows the vehicle to immediately switch to sensor-based localization using the pre-acquired environmental information, maintaining both reliability and precision without interruption.
3Reliability
If multiple localization methods are fused, then system reliability is improved, but device complexity increases
Solution Approach 1:
The system implements dynamic complexity management by adjusting its operational complexity based on satellite signal conditions. When satellite signals are strong, the system uses only satellite-based localization (low complexity). When signals are weak or lost, it dynamically activates sensor fusion and static object detection (higher complexity), maintaining reliability while minimizing unnecessary complexity during normal operation.
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
Enables precise vehicle localization and control with centimeter-level accuracy even in areas with weak or no satellite signals, enhancing autonomous driving capabilities.
Implementation Method 1
The first sensor return is generated by at least one of a camera, a light detection and ranging device, or a radar device
Implementation Method 2
The first sensor return is generated by at least one of a camera, a light detection and ranging device, or a radar device
Data Source
AI summary
An advanced driver assistance system (ADAS) controls a vehicle using two or more of: static objects, a vehicle heading angle, or a vehicle curvature at least when a satellite navigation signal is weak or lost. Distances from the vehicle to the static objects are measured by sensor(s) to be the same in both coordinate systems, and the vehicle location is found as an intersection between circles in the ground coordinate system. The ADAS can use sums of transverse and longitudinal distances, with a vehicle heading angle or a vehicle curvature, to determine the vehicle location with a weak or lost satellite signal, such as using a linear vehicle model or a curvature-based vehicle model.