Kalman Filter Parameter Switching for Adaptive Cruise Control
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Solution Overview
Problem
Adaptive cruise control systems face reliability issues due to noise and uncertainty in sensor measurements, with Kalman filters tuned for one environment performing poorly in different conditions, leading to suboptimal performance in various environments.
Innovation Solution
An autonomous vehicle control system that uses a processor to determine and switch between different sets of Kalman filter parameters based on current environmental conditions, such as obstructed sky views or wooded locations, by integrating map data and sensor information to improve location accuracy and adapt to changing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Kalman filter parameters are tuned for one environment, then measurement uncertainty is reduced for that specific environment, but performance deteriorates when operating in different environments
Solution Approach 1:
The system dynamically switches between different Kalman filter parameter sets based on the current environment type detected by the processor. Instead of using fixed parameters, the system adapts parameters in real-time by comparing sensor measurements with map data to identify the environment (tunnel, open sky, foliage-covered) and selecting the appropriate parameter set from stored options.
Solution Approach 2:
The system stores multiple sets of Kalman filter parameters, each optimized for specific environmental conditions. The processor changes the active parameter set based on detected environment type, allowing the measurement uncertainty to be minimized for the current conditions while maintaining the ability to adapt to different environments.
2Reliability
If worst-case scenario parameters are maintained to compensate for environmental differences, then reliability across all environments is improved, but ADAS performance is negatively affected
Solution Approach 1:
Instead of using uniform worst-case parameters globally, the system applies local optimization by selecting specific parameter sets tailored to each detected environment type. Each parameter set is optimized for its specific environment (tunnel, open sky, foliage), ensuring both reliability in the current conditions and optimal ADAS performance without the penalties of conservative worst-case tuning.
3Device complexity
If a single set of Kalman filter parameters is used, then device complexity is reduced, but measurement accuracy deteriorates in varying environmental conditions
Solution Approach 1:
The system performs preliminary organization of multiple Kalman filter parameter sets in memory, each pre-configured for specific environment types. The processor detects the current environment and retrieves the appropriate pre-prepared parameter set, avoiding the need for complex real-time parameter optimization while maintaining high measurement accuracy across varying conditions.
Data Source
AI summary
The present application relates to a method and apparatus including a sensor to detect a current location, a memory for storing a first set of Kalman filter parameters associated with a first environmental condition and a second set of Kalman filter parameters associated with a second environmental condition, a processor for performing an assisted driving algorithm according to the first set of Kalman filter parameters, for receiving the current location, for determining a second set of Kalman filter parameters in response to the current location, for performing the assisted driving algorithm according to the second set of Kalman filter parameters, and for generating a control signal in response to the assisted driving algorithm according to the second set of Kalman filter parameters, and a vehicle controller operative to control a vehicle in response to the control signal.