Lane Change Assist Activation Using Sensor, Map, and Historical Data
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Solution Overview
Problem
Current methods for automatically controlling lane change assist functions in vehicles are limited by the need to determine suitable road types and traffic conditions, leading to restricted activation and potential errors in determining when the lane change assist function can be activated or deactivated.
Innovation Solution
A control unit evaluates a combination of sensor-based, historical, and map-based conditions to determine if the lane change assist function can be activated, using multiple algorithms and data sources to ensure reliability and accurate activation or deactivation of the function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple data sources and algorithms are used to evaluate conditions for lane change assist activation, then reliability is improved, but device complexity increases
Solution Approach 1:
The condition evaluation system is segmented into three distinct groups: sensor-based conditions, historical-based conditions, and map-based conditions. Each group processes specific types of data independently, allowing the system to maintain high reliability through multiple data sources while managing complexity through structured organization of evaluation criteria.
Solution Approach 2:
The control unit is designed to handle multiple types of data inputs (sensor data, historical data, map data) and process them through a unified condition evaluation framework. This multi-functional approach allows a single system to perform diverse evaluation tasks, improving reliability without proportionally increasing overall device complexity.
2Measurement precision
If multiple data sources and algorithms are used to evaluate conditions for lane change assist activation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The evaluation conditions are segmented into three categories (sensor-based, historical-based, map-based), each contributing specific precision aspects. Sensor-based conditions provide real-time spatial accuracy, historical-based conditions provide contextual accuracy, and map-based conditions provide road type accuracy. This segmentation allows the system to achieve high overall measurement precision while managing complexity through structured organization.
Solution Approach 2:
The control unit merges results from multiple independent condition evaluations (sensor data analysis, historical data analysis, map data analysis) to make the final activation decision. This combination of multiple precision measurements from different sources enhances overall accuracy while the systematic merging process helps manage the complexity of processing multiple data streams.
Data Source
AI summary
Provided are a control unit and a method in a control unit to automatically control a lane change assist function in a vehicle. Multiple conditions indicating that a lane change assist function are activated at a current position of the vehicle are evaluated in the control unit. The conditions are selected from sensor based conditions, historical based conditions, and map based conditions. Sensor based conditions are conditions based on sensor. Historical based conditions are conditions based on historical data received. Map based conditions are conditions based on digital map data. If conditions from at least two different groups of conditions are evaluated as met, a digital signal is provided in the control unit enabling activation of the lane change assist function in the vehicle.