Vehicle Object Tracking With Context-Aware State Prediction
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Solution Overview
Problem
Conventional object tracking systems for vehicles rely solely on sensor data, which may not be sufficient to generate accurate predictions for decision-making processes requiring a low margin of error, especially in complex environments.
Innovation Solution
An object tracking system that utilizes supplemental information, such as high-definition maps, sun position, and environmental conditions, to refine predictions of an object's future state by applying factors like speed reduction, thereby improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object tracking systems rely solely on sensor data, then the system complexity remains low, but the prediction accuracy is insufficient for decision-making processes requiring a low margin of error
Solution Approach 1:
The patent merges sensor data with supplemental information from multiple sources (high-definition maps, environmental data, object attributes) to create a comprehensive prediction system. This combination allows the system to achieve higher prediction accuracy by integrating diverse data types that complement each other, resolving the contradiction between maintaining low system complexity and achieving high prediction accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing supplemental information (such as high-definition map data and environmental conditions) before they are needed for prediction. This allows the system to have prediction-ready data available, improving response time and accuracy without adding complex real-time processing requirements, thus resolving the contradiction between prediction accuracy and system complexity.
2Reliability
If sensor data alone is used for object tracking, then the data processing requirement is low, but the prediction accuracy is insufficient for safe vehicle decision-making
Solution Approach 1:
The patent extracts only the most relevant features and attributes from the supplemental information sources (such as speed reduction factors from high-definition maps, relevant environmental conditions, and object-specific attributes). This selective extraction approach provides sufficient data to improve prediction reliability while avoiding the burden of processing excessive data quantities, thus resolving the contradiction between prediction reliability and data quantity.
Solution Approach 2:
The system applies local quality by tailoring the type and amount of supplemental information used based on the specific prediction context, object type, and environmental conditions. Different objects and situations receive customized data sets with appropriate detail levels, improving prediction reliability for critical cases while reducing unnecessary data processing for less critical scenarios, thereby resolving the contradiction between prediction reliability and data quantity.
Data Source
AI summary
System, methods, and embodiments described herein relate to predicting a future state of an object detected in a vicinity of a vehicle. In one embodiment, a method for predicting a state of an object includes detecting, at a plurality of discrete times [t, t−1, t−2, . . . ], a respective plurality of states of the object, obtaining, based at least in part on a present location of the vehicle, supplemental information, associated with an environment of the present location, that indicates at least a speed reduction factor, executing a prediction operation to determine a predicted state of the object at a time t+1 based at least in part on the detected plurality of states and the supplemental information, determining an actual state of the object at a time t+1 based on data from the one or more sensors, and modifying the prediction operation based at least in part on the actual state.