Difference Map Encoding for Dynamic Object Tracking
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Solution Overview
Problem
Existing vehicle sensor systems face challenges in accurately perceiving motion due to errors and noise in sensor data, leading to inefficient and computationally intensive methods for distinguishing moving objects, particularly in mobile platforms like vehicles.
Innovation Solution
A system that generates a difference map encoding temporal changes in the surrounding environment, allowing for efficient tracking of dynamic objects by selectively storing relevant data and discarding redundant information, thereby improving situational awareness and reducing computational burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full frames of sensor data are stored and compared point-by-point to identify differences, then motion detection accuracy is improved, but computational complexity and data storage requirements increase significantly
Solution Approach 1:
The patent extracts only the essential motion information from full sensor frames by comparing occupancy maps and encoding temporal changes in a difference map. Instead of processing all point cloud data, the system extracts occupancy state changes (occupied, free, unknown) and encodes only the differences between frames, significantly reducing computational complexity while maintaining motion detection accuracy.
2Loss of information
If full frames of sensor data are stored for comparison, then complete environmental information is preserved, but data storage requirements become impractical for mobile platforms
Solution Approach 1:
The system extracts only the necessary environmental information by representing the scene as an occupancy map with discrete states (occupied, free, unknown) rather than storing complete point cloud frames. This extraction approach preserves essential spatial and temporal information while reducing storage requirements to a manageable level for mobile platforms.
Solution Approach 2:
The patent discards redundant sensor data by comparing occupancy maps and encoding only temporal changes in a difference map. The system recovers motion information from these encoded differences, maintaining the ability to detect and track moving objects while significantly reducing the quantity of stored data.
3Reliability
If sensor data is processed to distinguish moving objects, then situational awareness is improved, but noise and errors in sensor data create false detections
Solution Approach 1:
The system performs preliminary processing by generating occupancy maps that categorize spatial locations into discrete states (occupied, free, unknown) before comparing frames. This preliminary action filters out much of the sensor noise and errors, as the occupancy representation is more robust to small variations in sensor data than raw point cloud comparisons.
Solution Approach 2:
The patent discards noisy sensor data by encoding only the temporal changes in occupancy states in the difference map. By focusing on state transitions rather than raw sensor values, the system recovers reliable motion information while filtering out false detections caused by sensor noise and errors.
Data Source
AI summary
System, methods, and other embodiments described herein relate to tracking dynamic objects in a surrounding environment of a vehicle. In one embodiment, a method includes, in response to acquiring sensor data from at least one sensor, generating a current occupancy map that indicates locations of occupied grid cells as identified by the sensor data. The method includes updating a difference map according to the current occupancy map. The difference map encodes temporal changes in relation to prior states of occupancy of the grid cells to track dynamic objects in the surrounding environment over a defined temporal horizon. The method includes computing dynamics of the dynamic objects according to the difference map. The method includes providing the dynamics to at least one vehicle system within the vehicle.