Trajectory Channel Representation for Autonomous Behavior Prediction
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Solution Overview
Problem
Current autonomous vehicle systems face challenges in accurately predicting the behavior of target agents, such as pedestrians or other vehicles, which can lead to inefficient decision-making and potential collisions, due to the complexity of processing trajectory data and the need for laborious hand-crafted feature engineering.
Innovation Solution
A system that uses a convolutional neural network to process trajectory data represented as two-dimensional channels, allowing for efficient computation and accurate prediction of future agent trajectories, including the generation of probabilities for possible driving decisions, by integrating historical data and motion parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hand-crafted feature engineering is used for trajectory data processing, then prediction accuracy can be maintained through manual feature selection, but the system complexity and computational resource consumption increase significantly
Solution Approach 1:
The patent replaces manual hand-crafted feature engineering (mechanical process) with automated convolutional neural network processing (intelligent system). The CNN automatically learns and extracts relevant features from raw trajectory data, eliminating the need for manual feature selection and engineering while maintaining or improving prediction accuracy.
Solution Approach 2:
The convolutional neural network performs self-service by automatically learning optimal feature representations from the input trajectory data without human intervention. The system adapts and optimizes its own feature extraction process during training, reducing dependency on expert manual feature engineering.
2Loss of information
If complex hand-crafted feature engineering is performed, then detailed trajectory features can be extracted, but the time and computational resources required for processing increase
Solution Approach 1:
The patent substitutes time-consuming manual feature engineering with parallelized neural network computation. The CNN processes trajectory data through multiple convolutional layers simultaneously, extracting comprehensive features much faster than sequential manual analysis while preserving all relevant information.
Solution Approach 2:
The convolutional neural network performs preliminary feature extraction and processing in advance during the training phase, learning optimal feature representations that can be quickly applied during real-time prediction. This pre-computation reduces online processing time while maintaining feature completeness.
3Measurement precision
If traditional trajectory processing methods are used, then computational accuracy can be maintained, but the computational resource consumption and processing efficiency decrease
Solution Approach 1:
The patent replaces traditional computational methods with neural network-based processing that achieves superior efficiency. The CNN architecture is optimized for parallel computation on modern hardware (GPUs, TPUs), enabling fast processing of trajectory data while maintaining or improving prediction accuracy through learned patterns.
Solution Approach 2:
The patent changes the computational parameters and approach by using learned convolutional kernels and activation functions instead of fixed traditional algorithms. This allows the system to adapt computation to the specific characteristics of the data, improving both accuracy and efficiency simultaneously.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a representation of a trajectory of a target agent in an environment. In one aspect, the representation of the trajectory of the target agent in the environment is a concatenation of a plurality of channels, where each channel is represented as a two-dimensional array of data values. Each position in each channel corresponds to a respective spatial position in the environment, and corresponding positions in different channels correspond to the same spatial position in the environment. The channels include a time channel and a respective motion channel corresponding to each motion parameter in a predetermined set of motion parameters.