Vehicle Parking Notification via Trajectory Probability Maps
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Solution Overview
Problem
Current systems fail to effectively provide smart notifications to avoid collisions in tight structural environments like parking garages, relying on costly mapping or ineffective real-time sensor data.
Innovation Solution
A method that uses historical vehicle-trajectory data to create a probability distribution bitmap and a topographic map with notification zones, predicting future vehicle trajectories and generating notifications to prevent collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If costly real-time mapping or sensor data is used, then collision avoidance capability is improved, but system cost increases
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical trajectory data before the vehicle reaches the parking garage. This pre-acquired data is then processed to create probability distribution maps and notification zones that guide the vehicle through the structure, eliminating the need for costly real-time mapping or sensor data processing while maintaining collision avoidance capability.
2Measurement precision
If real-time sensor data is processed, then trajectory prediction accuracy is improved, but computational resources are consumed
Solution Approach 1:
The system performs preliminary computation by processing historical trajectory data offline to create probability distribution bitmaps and notification zones. During actual vehicle operation, the system simply queries these pre-computed maps rather than performing complex real-time sensor processing, thereby maintaining trajectory prediction accuracy while significantly reducing computational resource consumption.
Solution Approach 2:
The system creates a simplified copy of the complex real-time sensor data processing by using pre-computed probability distribution maps derived from historical data. This copy allows the system to make trajectory predictions without the computational burden of processing actual sensor data in real-time, maintaining accuracy while reducing energy consumption.
3Reliability
If historical trajectory data is collected and processed, then notification accuracy is improved, but data storage requirements increase
Solution Approach 1:
The system extracts only the essential information from historical trajectory data by processing it into probability distribution bitmaps and notification zones. This extraction process filters out redundant information and retains only the critical spatial and probabilistic data needed for accurate notifications, thereby reducing data storage requirements while maintaining notification accuracy.
Solution Approach 2:
The system changes the parameters of the stored data by transforming raw historical trajectory information into processed probability distribution maps with simplified spatial representations. This parameter transformation reduces the volume of data that needs to be stored while preserving the essential information for accurate collision avoidance notifications.
Data Source
AI summary
A method can be used to provide smart notifications to avoid collisions while the vehicle maneuvers in a tight structural environment, such as a home garage or an underground parking lot. The method includes receiving historical vehicle-trajectory data. The historical vehicle-trajectory data includes the location and the heading of the vehicle for each of the plurality of historical trajectories along the structure. The method further includes clustering the plurality of historical trajectories of the vehicle along the structure by types of maneuvers to generate a plurality of trajectory clusters. The method also includes creating a probability distribution bitmap using the plurality of trajectory clusters and creating a topographic map based on the probability distribution bitmap.