Parked car classification based on a velocity estimation
Find Innovative SolutionsGenerate Solutions
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 based on velocity and environmental features.
Innovation Solution
An end-to-end framework that integrates velocity estimation with a parked vehicle classification system, using a flow model to determine vehicle velocity and a classification model to identify parked vehicles based on velocity and environmental features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional separate systems are used for velocity estimation and parked vehicle classification, then system complexity is reduced, but measurement precision and reliability of parked vehicle recognition deteriorate
Solution Approach 1:
The patent merges the velocity estimation system and parked vehicle classification system into a single integrated end-to-end framework. The flow model estimates velocity from environmental representations, and the parked vehicle classification model uses this velocity along with environmental features to classify vehicles. This integration allows velocity information to directly inform classification decisions, improving measurement precision through coordinated optimization of both functions within one system.
2Reliability
If an integrated end-to-end framework is used for velocity estimation and parked vehicle classification, then measurement precision improves, but device complexity increases
Solution Approach 1:
The integrated framework is segmented into distinct functional modules: a flow model for velocity estimation that processes environmental representations, and a parked vehicle classification model that consumes velocity estimates along with environmental features. This segmentation allows each module to be optimized independently while maintaining reliable end-to-end integration, where the velocity information flow directly supports classification reliability without creating unmanageable system complexity.
3Measurement precision
If velocity estimation is integrated into parked vehicle classification, then classification accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The flow model performs velocity estimation as a preliminary action before the parked vehicle classification occurs. By estimating velocity from environmental representations in advance and providing it as input to the classification model, the system prepares classification-relevant information beforehand. This preliminary velocity estimation enables the classification model to make more accurate decisions without requiring additional processing time during the actual classification step, as velocity information is already available.
Data Source
AI summary
A method for controlling an ego vehicle in an environment includes detecting one or more changes in a position of an agent vehicle over time in accordance with capturing at least a first representation of the environment and a second representation of the environment via one or more sensors associated with the ego vehicle. The method also includes determining a velocity of the object based on detecting the one or more changes. The method further includes classifying the agent vehicle as parked based on the velocity and contextual data associated with the agent vehicle and/or the environment. The method still further includes planning a trajectory for the ego vehicle based on classifying the agent vehicle as parked. The method also includes controlling the ego vehicle to navigate along the trajectory.