Predictive Elevation Profiling for Automated Driving Trajectory Control
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Solution Overview
Problem
Existing vehicle control systems provide sub-optimal estimates for road bank and grade angles, leading to sub-optimal control performance during road elevation transitions, as they rely on single-point estimates rather than predictive profiles.
Innovation Solution
A method and system that utilize sensor data, location data, and map data to generate a predictive road elevation profile, including bank and grade angles, over a receding prediction horizon, enabling proactive control of vehicle dynamics, such as lateral and longitudinal movements, based on transformed elevation profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-point estimate is used for road bank and grade angles, then the system complexity is reduced, but the control performance becomes sub-optimal
Solution Approach 1:
The system generates a predictive road elevation profile ahead of the vehicle's current position, anticipating upcoming bank and grade angle changes rather than reacting to current conditions. This preliminary action allows the control system to prepare for future road geometry changes, improving control performance while maintaining reasonable system complexity through efficient prediction algorithms
2Reliability
If predictive road elevation profile is generated, then the control performance is improved, but the computational complexity increases
Solution Approach 1:
The predictive road elevation profile is generated over a receding prediction horizon that divides the future path into discrete time steps or segments. This segmentation allows the complex prediction problem to be broken down into manageable computational tasks, improving control performance while keeping computational complexity tractable through structured decomposition
3Stability of the object's composition
If pro-active control is implemented, then the vehicle stability is improved, but the processing requirements increase
Solution Approach 1:
The system employs dynamic prediction horizons and adaptive control strategies that adjust computational effort based on driving conditions. During normal conditions, simpler models are used, while during critical elevation transitions, more sophisticated predictions are activated. This dynamic approach improves vehicle stability during critical moments while reducing average processing requirements
Data Source
AI summary
In exemplary embodiments, methods, systems, and vehicles are provided that include: one or more sensors disposed onboard a vehicle and configured to at least facilitate obtaining sensor data for the vehicle; one or more location systems configured to at least facilitate obtaining location data pertaining to a location of the vehicle; a computer memory configured to store map data pertaining to a path corresponding to the location; and a processor disposed onboard the vehicle and configured to at least facilitate: generating an elevation profile along the path using the sensor data and the map data; and providing instructions for controlling the vehicle using the elevation profile.