Personalized Adaptive Cruise Control for Real-Time Following Gap Learning
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Solution Overview
Problem
Existing adaptive cruise control (ACC) systems have reduced accuracy and are unsuitable for real-time applications due to their reliance on historical data, failing to consider internal vehicle factors such as driver behavior, passenger types, and feedback, which affects their ability to accurately determine a driver's preferences and intentions.
Innovation Solution
A personalized ACC system that utilizes machine learning models to adjust its operation based on internal vehicle data, such as in-cabin characteristics and external data like weather and traffic conditions, allowing it to learn and adapt to a driver's preferences by capturing and analyzing real-time vehicle dynamics data, including manual interventions, to improve following gap management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing ACC systems rely on historical data to determine driver preferences, then they can provide basic cruise control functionality, but their accuracy is reduced and they are unsuitable for real-time applications
Solution Approach 1:
The system performs preliminary data collection and analysis by capturing in-cabin characteristics, weather conditions, and traffic conditions before ACC operation begins. This pre-processing of environmental and contextual data allows the system to establish baseline driver preferences in advance, enabling accurate real-time adjustments without computational delays during actual cruise control operation.
Solution Approach 2:
The system implements continuous feedback loops that monitor driver manual interventions to ACC operations and use this feedback to refine preference determinations. By constantly analyzing the discrepancy between automated ACC decisions and actual driver actions, the system progressively improves accuracy while maintaining real-time responsiveness through iterative learning rather than relying solely on historical data.
2Adaptability or versatility
If existing ACC systems use preset following distances manually selected by drivers, then the system structure remains simple, but the system cannot adapt to varying driver preferences and conditions
Solution Approach 1:
The system segments the following distance parameter into multiple adjustable dimensions including base following distance, weather-based adjustments, traffic-based adjustments, and driver preference modifications. This segmentation allows the complex adaptability requirements to be broken down into manageable computational components, each handling a specific aspect of following distance determination without overwhelming system complexity.
Solution Approach 2:
The system transitions from static preset following distances to dynamic, continuously adjustable parameters that adapt in real-time based on captured environmental data and learned driver preferences. The following distance becomes a living parameter that evolves with each driving session, automatically adjusting to weather conditions, traffic patterns, and individual driver behavior without requiring manual reconfiguration.
3Measurement precision
If existing ACC systems do not consider internal vehicle factors such as driver behavior and passenger types, then the system operation remains straightforward, but the system fails to accurately determine driver preferences and intentions
Solution Approach 1:
The system merges multiple data sources including in-cabin sensors, weather services, traffic information systems, and driver behavior monitoring into a unified contextual awareness framework. By combining these diverse data streams and processing them through integrated machine learning models, the system achieves accurate driver intention determination while managing complexity through unified architecture rather than separate independent systems.
Solution Approach 2:
The system employs machine learning models that automatically learn and adapt to driver preferences without requiring manual programming or configuration. The system serves itself by continuously training on captured data, automatically refining its understanding of driver intentions and preferences over time, thereby reducing the complexity burden on system designers and operators while improving accuracy.
Data Source
AI summary
The disclosure generally includes systems and methods of generating a preferred personalized adaptive cruise control (P-ACC) mode of operation. The method includes capturing vehicle data using one or more internal and external vehicle sensors, and adjusting one or more P-ACC mode of operation parameters, based on captured vehicle data, before operation of the P-ACC mode of operation. Vehicle data includes internal vehicle data comprising a type of passenger and number of passengers in the vehicle.