Adaptive Cruise Control Speed Profiling for Trigger Events
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Solution Overview
Problem
Current Advanced Cruise Control (ACC) systems lack the ability to optimize vehicle performance in terms of fuel economy and passenger comfort in response to real-world trigger events, such as dynamic traffic signals and adjacent vehicles, due to limited adaptive capabilities.
Innovation Solution
A method and system utilizing a decoupling estimator module and a pretrained machine learning model to predict a speed profile for a host vehicle, incorporating data from vehicle sensors, target vehicle status, and personalized operator profiles to minimize fuel consumption and jerk, with iterative model training for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current ACC systems use fixed rules for responding to trigger events, then the system structure is simple and easy to implement, but the system lacks adaptability to real-world traffic events and cannot optimize vehicle performance
Solution Approach 1:
The patent transforms the static fixed-rule ACC system into a dynamic adaptive system by introducing a pretrained machine learning model that continuously learns from historical speed profile data and real-time sensor inputs, enabling the system to adapt its responses to various trigger events while maintaining manageable complexity through modular architecture
Solution Approach 2:
The system performs preliminary training of the machine learning model using historical data before deployment, pre-loading the model with learned patterns and behaviors. This preliminary action enables the system to immediately respond adaptively to trigger events without requiring complex real-time decision logic, thus improving adaptability while controlling complexity
2Use of energy by moving object
If ACC systems respond to trigger events with predetermined actions, then the response time is fast and deterministic, but fuel economy and passenger comfort cannot be optimized
Solution Approach 1:
The machine learning model is pretrained in advance on historical speed profile data to learn optimal fuel-efficient driving patterns and responses to various trigger events. This preliminary learning enables the model to quickly predict optimal speed profiles during real-time operation without requiring complex real-time calculations, thus improving fuel economy while maintaining fast response times
Solution Approach 2:
The system incorporates feedback mechanisms where the pretrained ML model continuously receives real-time sensor data and adjusts speed profile predictions based on actual vehicle performance and traffic conditions. This feedback loop enables optimization of fuel economy through learned patterns while maintaining deterministic response times through the model's trained prediction capabilities
3Adaptability or versatility
If the vehicle maintains strict adherence to fixed speed profiles, then passenger comfort is improved by minimizing sudden movements, but the system cannot adapt to dynamic traffic conditions
Solution Approach 1:
The system uses a dynamic pretrained machine learning model that adapts to changing traffic conditions while maintaining smooth vehicle operation. The model learns from historical data what constitutes comfortable acceleration and deceleration patterns, dynamically adjusting speed profiles to balance adaptability to trigger events with passenger comfort by minimizing jerky movements through learned smooth transitions
Data Source
AI summary
A method and system of road driving optimization, having vehicle sensors configured to collect external sensor data, vehicle-state data, and communications data; a control module configured to analyze the collected data to detect a trigger event, a status of a target vehicle, an achievable speed range, and instant traction force; a decoupling estimator module configured to analyze the trigger event, the status of the target vehicle, the achievable speed range, and a personalized driver profile to determine a maximum free flow distance and an arrival speed at the free flow distance; a machine learning model configured predict a speed profile of the host vehicle approaching the trigger event based on the determined free flow distance, the determined arrival speed at the free flow distance, and the instant traction force; and a cruise control system configured to implement the predicted speed profile.