Vehicle Localization Robustness Adaptation
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Solution Overview
Problem
Conventional vehicle localization systems are computationally intensive and inefficient, requiring high power consumption and complex processes, especially when adapting to varying environmental scenarios, which can impact the accuracy and robustness of vehicle positioning.
Innovation Solution
A method that adapts the robustness of the localization system based on the vehicle's scenario by adjusting parameters such as the number of objects considered, maximum evaluation distance, and data processing, allowing for dynamic adjustment of computational resources and algorithm selection to optimize sensor data allocation and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data from multiple sensors are combined and allocated to digital map for robust localization, then localization accuracy and robustness are improved, but computational intensity and device complexity increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the degree of robustness based on the detected scenario. The system changes parameters such as the level of sensor data processing, the extent of map matching, and the complexity of localization algorithms according to the current driving situation. In simple scenarios, fewer computational resources are allocated, while in complex scenarios, the system increases processing intensity to maintain localization accuracy.
Solution Approach 2:
The patent implements dynamics by making the localization system adaptive rather than static. The degree of robustness is dynamically adjusted based on real-time scenario assessment. The system transitions between different operational modes (e.g., from high-robustness mode in complex environments to low-robustness mode in simple environments), allowing the computational complexity to vary according to actual needs rather than maintaining constant high complexity.
2Measurement precision
If computational resources are increased for complex localization processing, then localization accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent uses parameter changes to control the relationship between accuracy and energy consumption. By adjusting the degree of robustness parameter, the system modifies computational intensity, data processing depth, and algorithm complexity. This allows the system to achieve sufficient localization accuracy for each scenario without consistently using maximum computational resources, thereby reducing overall energy consumption while maintaining required precision.
3Reliability
If the degree of robustness is increased for all scenarios, then localization reliability is improved, but computational efficiency decreases
Solution Approach 1:
The patent applies dynamics by implementing a scenario-dependent robustness adjustment mechanism. The system dynamically determines the appropriate degree of robustness based on the current scenario classification. In safe, simple scenarios, the system reduces robustness requirements to improve computational efficiency. In hazardous or complex scenarios, the system increases robustness to maintain reliability, thus optimizing the balance between these two parameters across different operating conditions.
Solution Approach 2:
The patent applies partial action by providing just enough robustness for each scenario rather than maximum robustness for all scenarios. The system assesses the minimum required robustness level needed for safe localization in the current context and applies only that level. This avoids the inefficiency of applying excessive robustness processing in scenarios where it is not necessary, thereby improving overall computational efficiency while maintaining sufficient reliability.
Data Source
AI summary
A method for a vehicle localization system includes providing vehicle sensor data including information for an environment and/or an ego movement of the vehicle. A digital map of the environment of the vehicle is provided and a scenario based on the environment of the vehicle is ascertained based on the sensor data and/or based on the digital map. A degree of robustness for the localization system is derived from the ascertained scenario, a current degree of robustness of the localization system is derived, and a current degree of robustness of the localization system is adapted on that basis. Using an allocation module of the localization system, sensor data are allocated to the digital map based on the adapted degree of robustness. Using a position-determination module of the localization system, a position and/or an orientation of the vehicle is/are ascertained based on the sensor data allocated to the digital map.