Vehicle Safety Control Using Predictive Tire-Ground Friction
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Solution Overview
Problem
Autonomous vehicles face challenges in determining real-time safety-related parameters, such as friction coefficients between tires and the ground, which affect their ability to adjust control systems for safe navigation.
Innovation Solution
A safety system is developed to predict friction coefficients in front of the vehicle, allowing the control system to adjust the autonomous vehicle's operations based on these predictions, including determining a reduced safety distance and potentially slowing down the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use real-time sensor data to determine safety parameters, then navigation safety is improved, but the system complexity and computational burden increase
Solution Approach 1:
The system performs preliminary determination of friction coefficients and safety parameters using sensor data before actual navigation decisions are required. By pre-calculating these safety parameters based on current environmental conditions, the system reduces real-time computational burden while maintaining high navigation safety through advance preparation of critical safety information.
2Reliability
If the vehicle slows down based on predicted friction coefficients, then safety is improved, but productivity and speed decrease
Solution Approach 1:
The system dynamically adjusts vehicle speed based on predicted friction coefficients and safety parameters rather than applying fixed speed limitations. By continuously adapting the speed control strategy to real-time friction predictions and environmental conditions, the system optimizes the balance between safety requirements and productivity, slowing down only when and where friction conditions necessitate it.
Data Source
AI summary
A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.