AI Auto Stop Control Adapting to Driving Patterns
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Solution Overview
Problem
Existing auto stop systems on vehicles often lead to inefficient fuel consumption and driver fatigue due to repeated engine stop-start cycles, especially in congested areas, as they apply a one-size-fits-all engine stop rule without considering specific driving situations.
Innovation Solution
An artificial intelligence apparatus that collects driving information, predicts the current driving situation, and controls the auto stop system using a personalized control model learned from user feedback, determining an optimal control mode to prevent indiscreet activation and improve fuel efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the auto stop system applies a one-size-fits-all engine stop rule, then fuel waste and pollution are reduced, but driver fatigue increases and fuel efficiency worsens in congested areas
Solution Approach 1:
The patent implements dynamic control of the auto stop system by using a neural network to continuously learn and adapt to individual driving patterns. The system transitions from a static, fixed rule-based approach to a dynamic system that adjusts engine stop decisions based on real-time driving conditions and learned user behavior, thereby reducing unnecessary stop-start cycles in congested areas while maintaining fuel efficiency benefits.
Solution Approach 2:
The system changes the control parameters of the auto stop system by incorporating multiple input variables (driving patterns, traffic conditions, vehicle state) into the neural network model. This allows the system to optimize the engine stop rule parameters adaptively rather than using fixed parameters, resolving the contradiction between fuel savings and driver fatigue by finding optimal parameter settings for different driving scenarios.
2Productivity
If the auto stop system frequently stops the engine in congested areas, then fuel efficiency improves, but driver fatigue increases and departure delay occurs
Solution Approach 1:
The system performs preliminary learning of driving patterns during normal operation to predict optimal engine stop decisions in advance. By using the neural network to analyze historical driving data and predict future driving behavior, the system can proactively adjust engine stop timing to avoid departure delays while maintaining fuel efficiency, resolving the contradiction between productivity and time loss.
Solution Approach 2:
The patent implements a feedback mechanism where the neural network continuously learns from actual driving outcomes and user preferences. The system monitors engine stop-start cycles, fuel consumption, and driver responses, then uses this feedback to refine future control decisions. This closed-loop feedback allows the system to optimize the balance between fuel efficiency and departure timing based on real-world performance data.
3Device complexity
If the auto stop system uses a fixed control rule, then the system complexity is low, but the adaptability to different driving situations is poor
Solution Approach 1:
The patent implements a self-learning control system where the neural network automatically adapts to individual driving patterns without requiring manual programming or complex configuration. The system serves itself by continuously learning from operational data and autonomously optimizing control parameters, thereby achieving high adaptability while keeping the overall system architecture relatively simple and avoiding the need for complex manual tuning mechanisms.
Data Source
AI summary
An embodiment of the present invention provides an artificial intelligence apparatus for controlling an auto stop function, including: an input unit configured to receive brake information and velocity information of a vehicle; a storage unit configured to store a control model for the auto stop function; and a processor configured to: acquire driving information comprising the brake information and the velocity information through at the input unit, acquire base data used for determining a control of the auto stop function from the driving information, determine a control mode for the auto stop function by using the base data and the control model for the auto stop function, and control the auto stop function according to the determined control mode, wherein the control mode is one of an activation mode which activates the auto stop function or a deactivation mode which deactivates the auto stop function.