Vehicle Trajectory Prediction Evaluation Using Position Feedback
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Solution Overview
Problem
Existing technologies face challenges in precisely predicting the trajectory of objects in a vehicle's environment, particularly for automated vehicles, as sensor data is inherently fuzzy and movement behavior is difficult to forecast accurately.
Innovation Solution
A method and device for evaluating a function that predicts an object's trajectory by determining its start position and parameters, comparing possible and actual positions over time, and adapting function parameters based on agreement, utilizing environment sensor systems and external processing units for validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensor data is used to predict object trajectory, then the system can operate with automated vehicles, but the prediction accuracy deteriorates due to fuzzy sensor data and difficult-to-forecast movement behavior
Solution Approach 1:
The system continuously compares predicted trajectory positions with actual sensor-measured positions and uses this feedback to adapt function parameters. This closed-loop feedback mechanism allows the prediction function to learn from discrepancies and improve accuracy over time, resolving the contradiction between automated operation and prediction precision.
Solution Approach 2:
The system adapts function parameters based on the agreement between predicted and actual positions. By dynamically changing parameters in response to prediction errors, the system maintains improved accuracy despite using fuzzy sensor data for automated vehicle operations.
2Measurement precision
If function parameters are adapted based on agreement between predicted and actual positions, then prediction accuracy is improved, but the system complexity increases due to continuous evaluation and adaptation processes
Solution Approach 1:
The prediction function performs self-adjustment by automatically adapting its parameters based on prediction errors. This self-service mechanism reduces the need for external manual tuning and complex control systems, achieving improved accuracy while managing system complexity through autonomous adaptation.
3Reliability
If the prediction function is continuously evaluated and refined, then the reliability of trajectory prediction is improved, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial adaptation by adjusting function parameters based on the degree of agreement between predicted and actual positions. Rather than complete re-evaluation, it performs selective parameter adaptation, achieving improved reliability while reducing computational overhead and processing time.
Data Source
AI summary
A method and device for evaluating a function for predicting a trajectory of an object in an environment of a vehicle. The method includes acquiring first environment data values representing the environment of the vehicle at a first point in time, and the first environment data values including a start position of the object and object parameters; determining a trajectory of the object as a function of the start position and the object parameters, the trajectory including a possible position of the object at a second point in time later than the first point in time; acquiring second environment data values representing the environment of the vehicle at the second point in time and including an actual position of the object; determining an agreement between the possible position and the actual position at the second point in time; and evaluating the function as a function of the agreement.