Adaptive Cruise Control Using RL for Driver-Specific Gap Control
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Solution Overview
Problem
Current adaptive cruise control systems do not effectively consider target vehicle behavior when implementing acceleration and deceleration requests, leading to unsuitable follow distances that do not align with the driver's preferences, and lack customization based on real-time and historical driver-vehicle interactions.
Innovation Solution
The implementation of a Reinforcement Learning (RL) method that uses a processor to receive vehicle inputs, identify target vehicle parameters, model state estimations, generate reward functions, and adjust control actions based on driver behavior data to optimize follow distances and improve ACC performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional cruise control systems use fixed gap distance settings, then the system structure remains simple, but the adaptability to different driver preferences and target vehicle behaviors deteriorates
Solution Approach 1:
The patent implements dynamic adaptation by training a neural network model online using reinforcement learning. The system continuously updates the policy network based on real-time driver responses to ACC actions, transforming the static gap distance control into a dynamic system that adapts to individual driver preferences and target vehicle behaviors without requiring complex manual configuration
Solution Approach 2:
The system performs self-learning by automatically collecting data from driver-vehicle interactions and using this data to train and update its own control policy. The neural network model learns optimal gap distance settings through reinforcement learning from real-time feedback, enabling the system to improve its performance autonomously without external intervention or additional hardware
2Measurement precision
If the system collects and processes real-time driver behavior data, then the customization accuracy improves, but the computational load and processing time increase
Solution Approach 1:
The system performs preliminary data collection during normal vehicle operation, accumulating driver behavior data in the background without interrupting vehicle control. The neural network model is trained offline using this pre-collected data, separating the data collection phase from the control execution phase to minimize real-time processing delays
Solution Approach 2:
The system implements continuous feedback loops where driver responses to ACC actions are collected and used to update the neural network policy. The feedback mechanism efficiently processes only critical interaction data (accelerator/brake inputs in response to ACC acceleration/deceleration) to update the model, balancing measurement precision with processing efficiency
3Productivity
If the ACC system adjusts follow distance dynamically, then the performance and driver satisfaction improve, but the control stability and safety predictability worsen
Solution Approach 1:
The system implements safety constraints and validation checks before applying dynamic gap distance adjustments. The neural network policy is trained with safety-oriented reward functions that penalize unsafe behaviors, and the system maintains minimum safe following distances as hard constraints. This cushioning approach ensures that dynamic adjustments improve performance while maintaining reliability through pre-established safety boundaries
Solution Approach 2:
The system dynamically changes control parameters (gap distance, acceleration rates) based on learned driver preferences and target vehicle behavior patterns. The neural network outputs adjusted control commands that modify traditional ACC parameters adaptively, allowing performance optimization while the underlying safety architecture maintains stability through constrained parameter adjustment ranges
4Adaptability or versatility
If the system implements online learning algorithms, then the customization capability improves, but the computational resources required increase
Solution Approach 1:
The learning process is segmented into offline training phases and online refinement phases. Extensive neural network training occurs offline using collected driver behavior data, creating a pre-trained policy model. During online operation, only lightweight policy updates and inference computations are performed, significantly reducing real-time computational resource requirements while maintaining customization capability
Data Source
AI summary
In various embodiments, methods, systems, and vehicle apparatuses are provided. A method for implementing adaptive cruise control (ACC) established by Reinforcement Learning (RL) including executing, by a processor, adaptive cruise control to receive a set of vehicle inputs about a host vehicle's operating environment and current operations; identify, by the processor, a target vehicle operating in the host vehicle environment and quantifying a set of target vehicle parameters about the target vehicle derived from sensed inputs; modeling a state estimation of the host vehicle and the target vehicle by generating a set of speed and torque calculations about each vehicle; generating a set of results from at least one reward function based on one or more modeled state estimations of the host and target vehicle; processing the set of results with driver behavior data established by RL to correlate one or more control actions to the driver behavior data.