Vehicle Driver Command Prediction for Dynamic Motion Control
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Solution Overview
Problem
Current model predictive controllers for vehicle motion control assume constant driver input commands, which is not accurate in dynamic road conditions, leading to sub-optimum predictive control actions.
Innovation Solution
A driver command predictor system that includes sensors, a controller, and a command prediction unit using a deep learning model to generate future driver commands based on actual commands and vehicle states, adjusting motion vectors to improve predictive control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If driver input commands are assumed constant over the prediction horizon, then the control system is simpler to implement, but the prediction accuracy deteriorates in dynamic road conditions
Solution Approach 1:
The system performs preliminary prediction of future driver commands using a deep learning model before the control horizon expires. This allows the controller to anticipate driver intentions and adjust motion vectors proactively, improving prediction accuracy without requiring complex real-time adjustments during the control horizon.
Solution Approach 2:
The system transitions from assuming constant driver commands to dynamically predicting time-varying driver commands. The deep learning model captures the dynamic nature of driver behavior, allowing the control system to adapt to changing driver intentions while maintaining manageable complexity through offline training.
2Measurement precision
If driver commands are predicted to vary over time, then prediction accuracy improves, but the computational complexity increases
Solution Approach 1:
The system replaces complex real-time computational mechanisms with a pre-trained deep learning model. The model is trained offline to capture driver behavior patterns, and during operation, it provides accurate predictions with reduced real-time computational burden, effectively substituting mechanical complexity with intelligent algorithms.
Solution Approach 2:
The deep learning model is trained in advance using historical driver command data and vehicle state information. This preliminary training phase allows the model to learn complex driver behavior patterns offline, so that during actual operation, the system can generate accurate predictions without requiring complex real-time computations.
3Reliability
If the prediction horizon is extended to capture dynamic driver reactions, then control optimality improves, but the time delay in control response increases
Solution Approach 1:
The system extends the prediction horizon to capture dynamic driver reactions while using the deep learning model to predict future commands in advance. This allows the controller to plan optimal motion vectors over an extended horizon without experiencing increased response delay, as the predictions are generated proactively before the control horizon expires.
Data Source
AI summary
A driver command predictor includes a controller, multiple sensors, and a command prediction unit. The controller is configured to command an adjustment of multiple motion vectors of a vehicle relative to a roadway in response to multiple actual driver commands and multiple future driver commands. The actual driver commands are received at a current time. The future driver commands are received at multiple update times. The update times range from the current time to a future time. The sensors are configured to generate sensor data that determines multiple actual states of the vehicle in response to the motion vectors as commanded. The command prediction unit is configured to generate the future driver commands at the update times in response to a driver model. The driver model operates on the actual driver commands and the actual states to predict the future driver commands at the update times.