Adaptive Vehicle Speed Control via Curve Learning
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Solution Overview
Problem
Existing methods for controlling road vehicle speed through curves fail to provide a natural and comfortable ride, as they rely on visual information and do not adequately consider various factors like curvature, vehicle characteristics, and driver competence, leading to potential safety issues and excessive fuel consumption.
Innovation Solution
A method that adapts tuning parameter settings for road vehicle speed adjustment control by learning from manual driving training data, using a training set of speed adjustment profiles and road segment data to calculate simulated profiles, compare residuals, and iteratively optimize tuning parameters through optimization, regression analysis, or machine-learning to minimize residuals, thereby selecting optimal settings for a more natural control feel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed deceleration control or reactive yaw-rate based control is used for curve speed adjustment, then vehicle speed control is provided, but the ride is not natural and comfortable for passengers
Solution Approach 1:
The system dynamically adjusts multiple control parameters including target velocity, deceleration rate, and acceleration rate based on curve characteristics (radius, banking angle, gradient) and vehicle state. This allows the control system to adapt to different curve scenarios, providing both safety and comfort by optimizing parameters for each specific situation rather than using fixed control strategies
Solution Approach 2:
The system proactively determines target velocity and control parameters before the vehicle enters the curve, using map data and curve characteristics to prepare the optimal speed adjustment profile in advance. This preliminary action allows smooth speed reduction before the curve, avoiding last-minute reactive control that causes discomfort
2Reliability
If driver relies on visual information and manual speed determination, then driver has control flexibility, but speed may be excessive causing safety issues and fuel consumption
Solution Approach 1:
The system continuously monitors actual vehicle velocity and compares it with the target velocity profile, using this feedback to adjust control commands in real-time. The control system modifies acceleration and deceleration commands based on the difference between actual and target states, ensuring safe speed control while optimizing energy usage by avoiding excessive braking and acceleration
Solution Approach 2:
The system dynamically adapts control parameters based on real-time vehicle state and curve characteristics, adjusting target velocity, deceleration rate, and acceleration rate to optimize both safety and energy efficiency. This dynamic control allows the vehicle to maintain optimal speed through curves, reducing fuel consumption compared to conservative fixed-speed approaches
Data Source
AI summary
A method of adapting tuning parameter settings of a system (2) functionality (3) for road vehicle (1) speed adjustment control starting from initially selected settings and applying a training set of speed adjustment profiles obtained from manually negotiated road segments and road segment data for these. For each of these road segments: —a simulated speed adjustment profile is calculated using the selected settings and the road segment data; —the manual and the simulated speed adjustment profiles are compared to obtain a residual; —a norm of the residual is calculated. For all of the road segments of the training set: —a norm of the norms of the residuals is calculated; —at least one of optimization, regression analysis or machine-learning is performed to minimize the norm of the norms of the residuals by selecting different settings and iterating the above steps. Settings rendering a minimal training set norm are selected.