Vehicle Control Envelope for Uncertainty-Aware Motion Limiting
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Solution Overview
Problem
Heavy-duty vehicles face challenges in managing motion safely and efficiently, particularly in hazardous scenarios, due to uncertainties in sensor data and predictive modeling, leading to risks of events like under/over-steering, skid, jack-knifing, and trailer swing.
Innovation Solution
A method for controlling heavy-duty vehicle motion that estimates the current and future vehicle state, accounting for measurement and predictive uncertainties, and defines a vehicle control envelope to limit motion capabilities proactively, using wheel slip and speed-based requests to motion support devices to maintain vehicle stability within operational limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If sensor input signals are used for vehicle control decisions, then navigation and vehicle control are improved, but the risk of erroneous control decisions increases due to sensor uncertainty
Solution Approach 1:
The system continuously monitors sensor input signals and their associated uncertainties, using this feedback to dynamically adjust control decisions. The control unit evaluates the uncertainty levels of sensor data in real-time and modifies control actions accordingly, ensuring reliable operation even when sensor quality varies.
Solution Approach 2:
The system changes the parameters of control decisions based on sensor uncertainty levels. When uncertainty is high, the system adjusts control parameters to be more conservative, and when uncertainty is low, it can utilize more aggressive control strategies, optimizing both safety and performance.
2Reliability
If the vehicle control envelope limits motion capabilities, then vehicle stability and safety are improved, but the vehicle's operational flexibility is reduced
Solution Approach 1:
The vehicle control envelope is dynamically adjusted based on real-time vehicle state and sensor uncertainty. The control limits are not fixed but adapt to current operating conditions, allowing the vehicle to maintain stability while preserving motion capabilities when conditions permit.
Solution Approach 2:
The system changes control envelope parameters based on vehicle state and uncertainty levels. When the vehicle is in a stable state with low uncertainty, the control envelope expands to allow greater motion capability. When instability or high uncertainty is detected, the envelope contracts to prioritize stability.
3Reliability
If predictive motion modeling is used to estimate future vehicle state, then proactive hazard prevention is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary predictive motion modeling to estimate future vehicle states before hazards actually occur. By proactively calculating potential future states and their uncertainties, the system can prevent hazards before they manifest, reducing the need for complex reactive control systems.
Data Source
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Figure 2D
AI summary
A method for controlling motion of a heavy-duty vehicle (100), the method comprising estimating a current vehicle state (st0) comprising at least velocity (vt0) and acceleration (at0), wherein the estimated current state is associated with a current state uncertainty, estimating a future vehicle state (st1) based on the current vehicle state (st0), and on a predictive motion model of the vehicle (100), wherein the future vehicle state (st1) is associated with a future vehicle state uncertainty, defining a vehicle control envelope representing an operational limit of the vehicle, wherein the vehicle control envelope defines a range of vehicle states, comparing the estimated future vehicle state (st1), and the associated future vehicle state uncertainty, to the vehicle control envelope, and, if a probability that the future vehicle state (st1) breaches the control envelope is above a configured threshold value, limiting a motion capability of the vehicle (100).