Automatic Overtake Threshold Adaptation From Driver Assertions
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Solution Overview
Problem
Existing automatic overtake systems in vehicles do not optimally implement lane changes, particularly in situations involving multiple target vehicles and varying driver preferences, leading to suboptimal overtaking decisions.
Innovation Solution
A method and system that utilize sensors and a processor to adjust threshold values for automatic overtake decisions based on driver inputs, including relative vehicle velocities and timer adjustments, to customize overtaking behavior according to driver preferences, using exponential and linear adjustments for short-term and long-term adaptations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed threshold values are used for automatic overtake decisions, then the system is simple to implement, but it cannot adapt to varying driver preferences and situations
Solution Approach 1:
The patent implements dynamic threshold values that automatically adjust based on driver behavior patterns. The system transitions from static, fixed thresholds to dynamic thresholds that evolve over time by monitoring driver assertions (acceptances or rejections of overtake recommendations). This allows the system to adapt to individual driver preferences without requiring manual configuration or complex user interfaces.
Solution Approach 2:
The system incorporates feedback mechanisms where driver responses to overtake recommendations are continuously monitored and used to adjust future recommendations. When drivers accept or reject overtake suggestions, this feedback is processed to modify threshold values, creating a closed-loop system that learns and adapts to driver preferences over time.
2Adaptability or versatility
If the system monitors multiple driver inputs and adjusts thresholds continuously, then adaptability improves, but processing complexity and computational load increase
Solution Approach 1:
The system performs preliminary actions by pre-defining threshold adjustment rules and algorithms before actual driver interactions occur. The framework for adapting to driver preferences is established in advance, with predetermined logic for how different driver behaviors should influence threshold values. This reduces real-time processing complexity by avoiding the need to make complex decisions during critical driving moments.
3Reliability
If the system uses conservative threshold values for safety, then safety improves, but the frequency of missed overtake opportunities increases
Solution Approach 1:
The system dynamically adjusts threshold values based on learned driver preferences rather than using fixed conservative values. This allows the system to optimize the balance between safety and overtake opportunities for each individual driver, rather than applying a one-size-fits-all conservative approach that may be overly cautious for some drivers.
Solution Approach 2:
The system serves itself by automatically learning and adapting to driver preferences without external intervention. Through continuous monitoring of driver assertions and automatic adjustment of thresholds, the system optimizes its own performance to achieve the right balance between safety and overtake efficiency for each driver's specific preferences and driving style.
Data Source
AI summary
In accordance with an exemplary embodiment, methods and systems are provided for controlling automatic overtake functionality for a host vehicle. In on such exemplary embodiment, a disclosed method includes: (i) obtaining, via a plurality of sensors, sensor data pertaining to the host vehicle and a roadway on which the host vehicle is traveling; (ii) determining, via a processor, when an automatic overtake is recommended, using the sensor data in conjunction with one or more threshold values; (iii) receiving driver inputs pertaining to the automatic overtake; and (iv) adjusting, via the processor, the one or more threshold values for the automatic overtake based on the driver inputs.