Autonomous Driving Trajectory Accuracy Evaluation Using Autoencoders
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Solution Overview
Problem
Autonomous driving systems face challenges in accurately predicting the future trajectory of targets due to uncertainties in target movement and reliance on current sensor confidence levels without considering future uncertainties.
Innovation Solution
A method and system using a pre-trained autoencoder model to analyze trajectory history data, determining reconstruction loss, rank of the trajectory history matrix, and target recognition confidence level to evaluate the accuracy of trajectory prediction, adjusting the time window of sampling based on driving environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trajectory prediction is based only on current sensor confidence level, then the evaluation process is simple, but the prediction accuracy is insufficient because future uncertainty is not considered
Solution Approach 1:
The system performs preliminary actions by training an autoencoder model on historical trajectory data before actual prediction. The model learns normal trajectory patterns in advance, enabling it to evaluate future uncertainty without real-time complex computations. This preliminary training phase stores the knowledge needed for accurate prediction while keeping runtime evaluation efficient.
Solution Approach 2:
The autoencoder model serves as an intermediary between raw sensor data and trajectory prediction accuracy evaluation. It transforms trajectory history data into a compressed representation and reconstructs it, using the reconstruction error as a measure of prediction reliability. This intermediary component handles the complex analysis of future uncertainty, shielding the main prediction system from computational complexity.
2Reliability
If the time window of sampling is fixed, then the system is simple to operate, but it cannot adapt to different driving environments which reduces prediction reliability
Solution Approach 1:
The system implements dynamic adaptability by adjusting the time window of sampling based on driving environments. Rather than using a fixed time window, the system modifies it according to environmental conditions, making the prediction system flexible and context-aware. This dynamic adjustment improves reliability in varying conditions while the adjustment logic remains automated to maintain ease of operation.
Solution Approach 2:
The system changes the sampling time window parameter according to different driving environments. By modifying this key parameter dynamically, the system adapts to varying conditions such as urban vs. highway driving, improving prediction reliability without requiring complex manual configuration. The parameter change is driven by environmental assessment rather than user intervention.
Data Source
AI summary
An apparatus for controlling autonomous driving of a vehicle is introduced. The apparatus may comprise a processor and a memory configured to store one or more instructions, when executed by the processor, configured to cause the apparatus to store trajectory history data of a target object, generate, based on the trajectory history data, a trajectory history matrix for a time window of a sampling, input the trajectory history matrix into a machine learning model to determine reconstruction loss, wherein the machine learning model may comprise an autoencoder trained based on previous trajectory history data associated with movement of at least one object, determine, based on the reconstruction loss, a trajectory prediction accuracy, generate a signal indicating the trajectory prediction accuracy, and control, based on the signal, the autonomous driving of the vehicle.