Self-Learning Lane Change Assist for Driver-Specific Comfort
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Solution Overview
Problem
Current Lane Change Assist systems in vehicles often behave differently from drivers' habits, leading to uncomfortable driving experiences, even in automated driving systems developed with lower automation levels.
Innovation Solution
The development of a Self-Learning Active Lane Change Assist system that learns and adapts to manual-driving lane change habits by computing representative parameters and planning autonomous-driving lane change trajectories based on these habits, enhancing the system's behavior to resemble driver-specific habits in terms of lateral dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If Lane Change Assist systems use standardized autonomous driving algorithms, then automation level is improved, but driving comfort and familiarity deteriorate due to mismatch with individual driver habits
Solution Approach 1:
The system dynamically adapts the autonomous lane change behavior to match the driver's individual habits by learning from manual driving patterns. The lane change duration and trajectory are adjusted in real-time based on the detected driver preferences, making the automated system flexible and adaptable rather than rigid and standardized.
Solution Approach 2:
The system implements a feedback mechanism where the autonomous driving system continuously monitors and learns from the driver's manual lane change behaviors. By analyzing the driver's habitual patterns in terms of lane change duration and lateral dynamics, the system feeds this information back to customize its autonomous responses, creating a closed-loop adaptation process.
2Ease of operation
If Lane Change Assist systems implement driver-specific customization, then driving comfort is improved, but system complexity increases due to learning and adaptation mechanisms
Solution Approach 1:
The system performs self-learning and self-customization by automatically analyzing the driver's manual lane change patterns and adapting its own behavior accordingly. No manual configuration or programming is required from the driver - the system serves itself by autonomously detecting habits and adjusting parameters, reducing the burden on the user despite the increased functionality.
Solution Approach 2:
The system creates a virtual model or copy of the driver's lane change behavior patterns by analyzing manual driving data. It replicates the driver's habitual characteristics in terms of lane change duration and lateral dynamics, effectively copying the driver's style to guide the autonomous vehicle's responses without requiring complex rule-based programming.
3Reliability
If autonomous driving systems use fixed lane change parameters, then system reliability is improved, but adaptability to different driver preferences deteriorates
Solution Approach 1:
The system changes its operational parameters dynamically based on the detected driver habits. Instead of using fixed lane change durations and lateral acceleration profiles, the system adjusts these parameters according to the specific driver's preferences, transforming a static parameter set into a dynamic, adaptive configuration that maintains reliability through consistency with driver expectations.
Solution Approach 2:
The system performs preliminary learning and analysis of the driver's lane change patterns during manual driving phases. By pre-processing and storing the driver's habitual characteristics before autonomous operation begins, the system prepares customized parameters in advance, ensuring reliable and personalized performance from the start of autonomous mode without requiring real-time adjustments during critical maneuvers.
Data Source
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AI summary
An automotive active lane change assist (HCLA) designed to cause a motor vehicle (1) to carry out autonomous-driving lane change manoeuvres and to customize the autonomous-driving lane change manoeuvres based on manual-driving lane change habits of a driver of the motor vehicle (1) learnt during one or different manual-driving sessions of the motor vehicle (1). The automotive active lane change assist (HCLA) is further designed to customize the autonomous-driving lane change manoeuvres based on manual-driving lane change habits of a driver of the motor vehicle (1) by determining one or different autonomous-driving lane change settings for one or different drivers of the motor vehicle (1) and for one or different types or categories of roads along which the motor vehicle (1) can carry out autonomous-driving lane change manoeuvres.