AI Auto Stop Control for Engine Ignition Timing
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Solution Overview
Problem
Existing auto stop systems delay vehicle departure by requiring user intervention to restart the engine after stopping, leading to user fatigue and inefficient fuel consumption.
Innovation Solution
An artificial intelligence apparatus and method that uses traffic and driving information to automatically determine the optimal engine ignition timing, allowing for personalized control based on user feedback, and updates the auto stop system control model to improve satisfaction and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the auto stop system stops the engine when the vehicle stops and requires user intervention to restart, then fuel waste and pollution are reduced, but vehicle departure is delayed and user fatigue increases
Solution Approach 1:
The system performs preliminary action by automatically determining and executing engine restart before the user would need to manually restart it. The control device monitors vehicle stop conditions and autonomously decides when to restart the engine based on stored restart conditions, eliminating the delay caused by waiting for user intervention while maintaining fuel efficiency by not restarting during unnecessary stops.
Solution Approach 2:
The system implements self-service by enabling the auto stop system to autonomously manage engine restart operations without requiring user input. The control device stores restart conditions and automatically compares current vehicle states against these conditions to determine when restart is necessary, allowing the system to serve itself by making intelligent restart decisions based on traffic information, vehicle status, and learned user preferences.
2Loss of time
If the auto stop system automatically ignites the engine without user intervention, then vehicle departure delay is reduced, but system complexity increases
Solution Approach 1:
The system applies feedback by continuously monitoring vehicle stop conditions, comparing them against stored restart conditions, and adjusting engine restart decisions based on the outcomes. The control device receives feedback from various sensors about vehicle status, traffic conditions, and user responses to refine its automatic restart timing, reducing departure delay while managing complexity through intelligent decision-making rather than mechanical complexity.
Solution Approach 2:
The control device serves multiple functions: it monitors vehicle stop conditions, stores and retrieves restart conditions, processes traffic information, learns user preferences, and executes engine restart commands. By consolidating these diverse functions into a single multi-functional control device, the system reduces overall complexity compared to having separate dedicated components for each function while still achieving automatic engine ignition to eliminate departure delay.
3Loss of energy
If the auto stop system uses traffic information and driving information to determine engine ignition timing, then fuel efficiency is optimized, but information processing requirements increase
Solution Approach 1:
The system performs preliminary action by pre-storing restart conditions and traffic information patterns that have been determined to optimize fuel efficiency. When a vehicle stop occurs, the control device quickly compares the current situation against these pre-processed conditions rather than analyzing all possible variables in real-time, reducing information processing requirements while maintaining optimized fuel efficiency through informed restart decisions.
4Adaptability or versatility
If the auto stop system personalizes control based on user feedback, then user satisfaction increases, but device complexity increases
Solution Approach 1:
The system implements feedback by collecting user responses to automatic restart decisions and using this feedback to refine future decisions. The control device monitors whether users manually override automatic restarts or express satisfaction/dissatisfaction, and adjusts the stored restart conditions and user preference profiles accordingly. This feedback loop enables personalization that adapts to individual user needs while managing complexity through iterative learning rather than complex upfront programming.
Solution Approach 2:
The system applies dynamics by making the restart conditions and control parameters adaptable and changeable based on user feedback and varying driving situations. Rather than using fixed, static restart criteria, the control device dynamically adjusts the stored restart conditions and user preference profiles over time, allowing the system to personalize control for each user while managing complexity through flexible, evolving parameters rather than rigid complex structures.
Data Source
AI summary
Disclosed is an artificial intelligence apparatus for controlling an auto stop function. The artificial intelligence apparatus includes an input unit configured to receive at least one of image information on surroundings of a vehicle, sound information on the surroundings of the vehicle, brake information of the vehicle, or velocity information of the vehicle; a storage unit configured to store an auto stop function control model; and a processor configured to obtain, via the input unit, input data related to at least one of traffic information or driving information, obtain base data used for determining control of the auto stop function from the input data, determine an engine ignition timing or an engine ignition setting using the base data and the auto stop function control model, and ignite the engine of the vehicle automatically according to the determined engine ignition timing or the determined of engine ignition setting, wherein the engine ignition timing is an indication of how much time is required for the engine to be ignited after the input data is obtained or after the time of the determination, and wherein the engine ignition setting is an indication of whether to ignite the engine at the time of acquiring the input data or at the time of the determination.