Personalized ACC Gap Control for Driver-Specific Following Distance
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Solution Overview
Problem
Existing Adaptive Cruise Control (ACC) systems have limited predefined settings and do not consider personalized driving styles and preferences, leading to uncomfortable experiences for drivers and reduced trust in driving automation.
Innovation Solution
A personalized ACC system that learns driver preferences using Maximum Entropy Inverse Reinforcement Learning (MaxEnt-IRL) and Model Predictive Controller (MPC) based on static and dynamic contextual factors, incorporating machine learning to recommend optimal gap distances between vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If predefined ACC settings are used, then system complexity is reduced, but adaptability to individual driving styles deteriorates
Solution Approach 1:
The patent implements dynamic adaptation by continuously learning driver preferences through reinforcement learning algorithms. The system transitions from static predefined settings to dynamic personalized parameters that adapt in real-time based on driver behavior patterns, thereby resolving the contradiction between system simplicity and adaptability.
Solution Approach 2:
The system performs self-learning and self-adjustment by automatically observing driver behavior and updating its control parameters without requiring manual intervention. This self-service capability enables the system to personalize ACC behavior for each driver while maintaining operational simplicity.
2Adaptability or versatility
If personalized learning algorithms are implemented, then adaptability to driving styles is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical or manual configuration systems with intelligent software-based reinforcement learning algorithms. This substitution enables personalized adaptation through data-driven learning rather than through complex hardware configurations, thereby achieving high adaptability with manageable system complexity.
Solution Approach 2:
The system dynamically adjusts control parameters based on learned driver preferences rather than requiring complex structural changes. By modifying software parameters and control algorithms based on observed behavior, the system achieves personalization while keeping the underlying hardware architecture relatively simple.
3Reliability
If remote control of gap distance is implemented, then driving safety is improved, but driver autonomy deteriorates
Solution Approach 1:
The system implements a feedback loop where driver responses to automated gap adjustments are continuously monitored and used to refine future control actions. This feedback mechanism ensures that the remote control function adapts to driver preferences over time, maintaining safety while progressively restoring perceived autonomy.
Solution Approach 2:
Instead of imposing fixed remote control settings on the driver, the system inverts the approach by having the driver's behavior shape the control parameters. The driver indirectly controls the system through their natural responses, transforming a top-down control mechanism into a bottom-up adaptive system that respects driver autonomy.
Data Source
AI summary
An example operation includes one or more of obtaining sensor data captured by one or more sensors of a transport while the transport is traveling behind a lead transport, detecting that a gap distance between the transport and the lead transport is outside a predetermined threshold based on the obtained sensor data, in response to the detection, determining a recommended gap distance between the transport and the lead transport, and controlling, via the server, a speed of the transport via an activated cruise control function based on the recommended gap distance.