Adaptive Convolution Kernel Selection for Vehicle Trajectory Prediction
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Solution Overview
Problem
Current machine learning techniques used in autonomous and semi-autonomous vehicles rely on abstract models of the environment, which do not effectively predict future events in real-world scenarios.
Innovation Solution
A vehicle system that uses one or more sensors to capture environmental data, determines structures within the environment, selects a parameterized kernel for predicting vehicle trajectories based on these structures, and performs a convolution operation to predict future vehicle trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If abstract models of the environment are used for prediction, then the system complexity is reduced, but the prediction accuracy deteriorates
Solution Approach 1:
The patent transforms the convolution kernel from a fixed, abstract parameter to a dynamic parameter that adapts based on detected environmental structures. The kernel parameters are modified according to the specific geometric and contextual features of the environment, allowing the system to maintain low computational complexity while achieving high prediction accuracy through context-aware parameter adaptation.
Solution Approach 2:
The system transitions from using static, pre-defined convolution kernels to dynamically selecting and adapting kernels based on real-time environmental structure detection. This dynamic adaptation allows the prediction model to respond to varying environmental conditions, improving accuracy without requiring a completely complex model architecture.
2Measurement precision
If environment-specific kernel parameters are learned and applied, then the prediction accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent segments the environment processing into distinct stages: environmental structure detection, kernel parameter selection, and convolution prediction. This segmentation allows each component to remain relatively simple while the integrated system achieves high accuracy. The kernel selection module independently handles parameter adaptation based on detected structures, preventing overall system complexity from escalating.
Solution Approach 2:
The system performs preliminary detection of environmental structures before selecting kernel parameters for prediction. This preliminary action allows the system to prepare context-appropriate parameters in advance, reducing the computational burden during the actual prediction phase and preventing complexity accumulation in real-time processing.
3Adaptability or versatility
If convolution kernels are selected based on environmental structures, then the adaptability is improved, but the processing time increases
Solution Approach 1:
The patent applies local quality by selecting convolution kernel parameters that are specifically adapted to the local environmental structure being analyzed. Rather than using a single global kernel for all situations, the system tailors kernel parameters to match the specific geometric and contextual characteristics of each environmental region, improving adaptability while maintaining efficient processing through localized parameter selection.
Data Source
AI summary
A vehicle system includes one or more sensors configured to capture aspects of an environment and a computing device. The computing device is configured to receive information about the environment captured by the one or more sensors, determine one or more structures within the environment based on the received information, select a kernel that is parameterized for predicting a vehicle trajectory based on the one or more structures determined within the environment, and perform a convolution of the selected kernel and an array defining the environment, wherein the convolution predicts a future trajectory of a vehicle within the environment.