Indoor Vehicle Parking Position Detection Using AI Route Correction
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Solution Overview
Problem
Connected vehicles face significant errors in determining driving routes and parking positions within indoor or underground areas without GPS information, with errors increasing as the vehicle travels longer distances.
Innovation Solution
An apparatus and method utilizing dead reckoning and machine learning to correct errors in driving route data by generating virtual data, which includes an AI model that compares actual and virtual driving route data to determine an accurate parking position within indoor parking lots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dead reckoning is used to generate driving route based on speed and direction data, then the system can operate without GPS in indoor parking lots, but the error in determining driving route and parking position increases as the vehicle travels longer distances
Solution Approach 1:
The system employs feedback mechanisms by continuously comparing the dead reckoning generated driving route with actual driving route data collected from the vehicle. The difference between these routes is used to train an AI model that learns to correct cumulative errors. This feedback loop enables the system to maintain accurate parking position detection even after long driving distances in GPS-denied environments.
Solution Approach 2:
The system creates a virtual copy of the driving route through dead reckoning algorithms that simulate vehicle movement based on speed and steering angle data. This virtual driving route serves as a reference model that can be compared against actual vehicle trajectories, enabling error detection and correction without requiring external GPS infrastructure.
2Measurement precision
If additional equipment is installed to improve position detection accuracy in indoor parking lots, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The system performs self-service by using the vehicle's existing sensors (speed sensor, steering angle sensor) to generate and correct its own driving route data. The AI model is trained on the vehicle's own operational data, enabling the system to improve its measurement precision using resources already available in the vehicle without requiring external infrastructure or additional specialized equipment.
Solution Approach 2:
The system achieves multi-functionality by using a single integrated approach that combines dead reckoning algorithms, AI error correction, and existing vehicle sensors to accomplish both navigation and parking position detection. This universal solution eliminates the need for separate specialized equipment for indoor positioning, reducing overall system complexity while maintaining high measurement precision.
Data Source
AI summary
An apparatus for detecting a parking position of a vehicle includes a first driving route module for generating a first driving route of a vehicle based on driving information including a speed and a direction of the vehicle through an indoor parking lot; a second driving route module for generating a second driving route by inputting the generated first driving route into an artificial intelligence model; and a parking position determining module for determining a final parking position of the vehicle in the indoor parking lot based on the generated second driving route.