Neural Network Object Behavior Prediction for Autonomous Vehicles
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Solution Overview
Problem
Current autonomous driving technologies face challenges in accurately predicting the behavior of surrounding objects, as conventional methods require iterative computation for each object, making the process difficult and time-consuming.
Innovation Solution
A data processing architecture comprising a multilayer perceptron (MLP) and a convolutional neural network (CNN) is used to predict the behavior of objects in an autonomous vehicle's environment, where historical features of objects and map information are processed to generate driving signals for controlling the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional iterative computation methods are used to predict object behavior, then prediction accuracy can be achieved, but the computational process becomes time-consuming and complex
Solution Approach 1:
The patent replaces the conventional iterative computational mechanics with a neural network-based predictive model. The neural network learns object behavior patterns from training data and directly predicts future states without requiring iterative computation, thereby maintaining prediction accuracy while significantly reducing computation time for autonomous vehicle navigation.
2Reliability
If conventional iterative computation is performed for each object, then comprehensive behavior prediction is achieved, but device complexity increases
Solution Approach 1:
The patent merges multiple object behavior predictions into a unified neural network model that processes all objects simultaneously. Instead of performing separate iterative computations for each object, the neural network takes multiple object features as input and predicts their behaviors in a single integrated computation, reducing system complexity while maintaining comprehensive prediction reliability.
3Adaptability or versatility
If iterative prediction computation is performed among all objects, then interaction prediction is achieved, but the process becomes difficult and time-consuming
Solution Approach 1:
The patent substitutes the complex iterative computational process with a neural network that has been trained to directly predict object interactions. The neural network learns interaction patterns from training data involving multiple objects and their relationships, enabling it to predict interactions in a single forward pass without requiring difficult iterative computation among all objects.
Data Source
AI summary
In one embodiment, a method, apparatus, and system may predict behavior of environmental objects using machine learning at an autonomous driving vehicle (ADV). A data processing architecture comprising at least a first neural network and a second neural network is generated, the first and the second neural networks having been trained with a training data set. Behavior of one or more objects in the ADV's environment is predicted using the data processing architecture comprising the trained neural networks. Driving signals are generated based at least in part on the predicted behavior of the one or more objects in the ADV's environment to control operations of the ADV.