Driver Action Prediction Using Bidirectional Recurrent Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current driver action prediction systems are limited by their inability to accurately and efficiently predict driver actions in advance due to limitations in model design, data usage, and the separation of prediction and recognition processes, leading to insufficient accuracy and horizon in anticipating driver behavior.
Innovation Solution
A driver action prediction system utilizing a deep bidirectional recurrent neural network for temporal fusion of sensor data, which separates features for recognition and prediction, and trains the prediction network using results from a recognition network, allowing for more sensing modalities and reduced human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing driver action prediction systems use limited sensor data and simple models, then device complexity is reduced, but prediction accuracy and prediction horizon deteriorate
Solution Approach 1:
The patent merges multiple sensing modalities (accelerometer, gyroscope, magnetometer, barometer, GPS, camera, microphone) into a unified prediction system that processes fused sensor data through neural networks, achieving high prediction accuracy while managing complexity through integrated architecture
Solution Approach 2:
The patent transitions from traditional single-direction prediction to bidirectional recurrent neural networks that process temporal sequences in both forward and backward directions, enabling the system to capture contextual patterns from past and future states simultaneously, thereby improving prediction horizon and accuracy
2Ease of manufacture
If prediction and recognition processes are separated, then each process can be optimized independently, but loss of information occurs due to lack of shared learning
Solution Approach 1:
The patent segments the driver action analysis into distinct prediction and recognition processes that operate independently yet can share sensor data processing pipelines, allowing each process to be optimized for its specific function while maintaining information flow through common feature extraction layers
Solution Approach 2:
The patent creates a universal sensor data processing framework that serves both prediction and recognition tasks, where the same preprocessed sensor features can be fed into different neural network models, eliminating redundant processing and preventing information loss while maintaining independent optimization capabilities
3Speed
If current sensor data only is used without access to stored sensor data, then processing speed is improved, but prediction horizon is limited
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing sensor data in structured formats during normal operation, creating a historical database that can be rapidly queried during prediction tasks, thus enabling extended prediction horizons without sacrificing real-time processing speed through efficient data retrieval mechanisms
Data Source
AI summary
By way of example, the technology disclosed by this document may be implemented in a method that includes receiving stored sensor data describing characteristics of a vehicle in motion at a past time and extracting features for prediction and features for recognition from the stored sensor data. The features for prediction may be input into a prediction network, which may generate a predicted label for a past driver action based on the features for prediction. The features for recognition may be input into a recognition network, which may generate a recognized label for the past driver action based on the features for recognition. In some instances, the method may include training prediction network weights of the prediction network using the recognized label and the predicted label.