Personalized Vehicle Control Signals Using Imitation Learning
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Solution Overview
Problem
Existing autonomous driving systems fail to personalize driving behavior to individual users, leading to discomfort and increased instances of driver intervention, which can result in unsafe situations due to lack of consideration for user preferences.
Innovation Solution
The implementation of imitation learning techniques to create a personalized driving policy by training a machine-learning model on human driver demonstrations, allowing the vehicle to adapt and imitate the driver's style, including parameters like following distance, lane changing, and acceleration profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a general machine-learning model is applied to improve automated driving performance for the whole fleet, then the automated driving performance is improved, but user comfort deteriorates because user preferences are not considered
Solution Approach 1:
The patent segments the automated driving system into two components: a general machine-learning model for fleet-wide performance improvement and a personalized driving style model for individual user preferences. This segmentation allows both general improvements and personalization to coexist, resolving the contradiction between fleet-wide reliability and individual user comfort.
Solution Approach 2:
The patent applies local quality by maintaining a personalized driving style model specific to each user that overrides or adjusts the general machine-learning model's decisions. This allows the system to provide locally optimized driving behavior tailored to each user's preferences while still benefiting from general improvements, thereby maintaining both fleet-wide performance and individual comfort.
2Productivity
If the automated driving system makes decisions without considering driver preferences, then the system operates efficiently, but driver comfort deteriorates leading to increased driver intervention
Solution Approach 1:
The patent implements feedback by continuously learning the driver's preferences through observed driving behavior and using this information to adjust the automated driving decisions. The system monitors driver reactions and intervening patterns, then adapts the driving style model accordingly, creating a feedback loop that improves driver comfort while maintaining system efficiency.
Solution Approach 2:
The system performs self-service by automatically adapting to each driver's preferences without requiring explicit programming or manual configuration. The machine-learning model autonomously learns driving styles from observed behavior and adjusts its decisions accordingly, maintaining efficiency while improving comfort through self-adjustment.
3Ease of operation
If the driver frequently intervenes in the automated driving system, then the driver maintains control, but safety deteriorates due to lack of situational awareness
Solution Approach 1:
The patent applies dynamics by making the automated driving system adaptable and flexible in response to driver preferences. By dynamically adjusting the driving style to match what the driver would naturally do, the system reduces the need for intervention while maintaining driver confidence and situational awareness, thereby improving safety without sacrificing control.
Data Source
Figure 1~2b
AI summary
The invention relates to methods, apparatuses and computer programs for generating a machine-learning model and for generating a control signal for operating a vehicle. The method for generating the machine-learning model comprises determining information about a driving behavior of a driver of the vehicle. The method comprises transforming the information about the driving behavior of the driver of the vehicle into a target function for the machine-learning model. The method comprises generating the machine-learning model. The machine-learning model is trained using an imitation learning approach that is based on the target function, to obtain a machine-learning model that imitates the driving behavior of the driver of the vehicle.