Parked Car Recognition via Flow-Based Velocity Estimation
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Solution Overview
Problem
Current systems for identifying parked cars in environments used by autonomous agents are not designed as end-to-end systems, leading to inefficiencies in classifying vehicles as parked or not, which can affect trajectory planning and decision-making in autonomous vehicles.
Innovation Solution
An end-to-end framework is introduced that integrates vehicle velocity with a parked car classification system, using a flow model to determine environmental flow and a velocity model to estimate vehicle velocity, combined with features from the vehicle and environment to classify vehicles as parked or not.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional separate systems are used for velocity estimation and parked car classification, then system complexity is reduced, but measurement precision and reliability of parked car identification deteriorate
Solution Approach 1:
The patent merges the velocity estimation module and parked car classification module into a single integrated end-to-end system. The velocity estimator and classification network share common components and process data through a unified architecture, allowing velocity information to be directly incorporated into the classification decision-making process, thereby improving identification accuracy while managing system complexity through consolidation.
Solution Approach 2:
The integrated system performs multiple functions simultaneously: it estimates vehicle velocity, classifies parked cars, and provides uncertainty quantification all within a single framework. The shared neural network components serve multiple purposes, extracting features for both velocity estimation and classification tasks, reducing overall system complexity while enhancing measurement precision.
2Reliability
If an end-to-end framework integrating velocity estimation is implemented, then parked car identification accuracy is improved, but device complexity increases
Solution Approach 1:
The end-to-end framework is segmented into distinct functional modules: an velocity estimation module, a classification module, and an uncertainty quantification module. Each module has a specific function but they are integrated through shared representations and coordinated processing, allowing the system to achieve high reliability through specialized sub-components while managing overall complexity through modular organization.
Solution Approach 2:
The system incorporates feedback mechanisms where the estimated velocity information is fed back into the classification process to refine parked car identification. The uncertainty estimates also provide feedback for adjusting classification thresholds and decision-making, improving reliability through iterative refinement while maintaining a structured framework that manages complexity.
Data Source
AI summary
A method controlling an ego vehicle in an environment includes determining, via a flow model of a parked vehicle recognition system, a flow between a first representation of the environment and a second representation of the environment. The method also includes determining, via a velocity model of the parked vehicle recognition system, a velocity of a vehicle in the environment based on the flow. The method further includes determining, via a parked vehicle classification model of the parked vehicle recognition system, the vehicle is parked based on the velocity of the vehicle and one or more of features associated with the vehicle and/or the environment. The method still further includes planning a trajectory of the ego vehicle based on determining the vehicle is parked.