Autonomous Vehicle Behavior Synchronization Using Driving Preference Models
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Solution Overview
Problem
Autonomous vehicles often deviate from user preferences in driving behaviors, leading to dissatisfaction and reduced usability, as their pre-programmed algorithms may not align with individual driving habits and preferences.
Innovation Solution
A system and method for autonomous vehicle behavior synchronization that analyzes a user's driving history to form a driving preference model, adjusting the vehicle's algorithms to match the user's preferences, using deep policy inference Q-networks and deep recurring policy inference Q-networks to recognize and replicate the user's driving patterns and habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles use pre-programmed base algorithms for driving operations, then basic driving functions are achieved, but user satisfaction deteriorates due to deviation from individual driving preferences
Solution Approach 1:
The system performs preliminary actions by collecting driving behavior data from multiple sources (telematics, social media, surveys) before the user even requests personalized service. The offline behavior generation module pre-processes this data to create baseline driving behaviors, so when the user enters the vehicle, the system already has a foundation to work with, reducing the complexity of real-time personalization.
Solution Approach 2:
The system implements continuous feedback loops where user responses to service requests and actual driving behaviors are fed back into the behavior generation module. This feedback mechanism allows the system to iteratively refine the driving behavior model, adjusting algorithms based on what the user accepts or rejects, thereby improving adaptability without requiring complete algorithm replacement.
2Measurement precision
If the system collects and analyzes extensive driving history data to form personalized models, then user satisfaction improves, but data processing time and computational resources increase
Solution Approach 1:
The system segments the driving behavior analysis into distinct modules: offline behavior generation from historical data, online behavior adjustment based on real-time feedback, and separate preference categorization (aggressive, conservative, eco-friendly). This segmentation allows parallel processing of different data streams and reduces the time penalty of comprehensive analysis by distributing computational load across multiple specialized components.
Solution Approach 2:
The system changes parameters by using weighted scoring systems to evaluate different driving behaviors against user preferences. Instead of complex probabilistic models, it transforms behavioral data into scored parameters that can be quickly compared and matched, reducing computational time while maintaining precision in preference identification through the behavior synchronization module.
3Ease of operation
If the autonomous vehicle adjusts driving algorithms to match user preferences, then riding comfort improves, but deviation from safe standard driving procedures may occur
Solution Approach 1:
The system implements dynamic adjustment where driving behaviors are not fixed but continuously adapted based on real-time feedback. The behavior generation module dynamically balances standard safe driving procedures with personalized comfort preferences, allowing the vehicle to be more aggressive or conservative only when the user explicitly requests and accepts such deviations, thereby maintaining safety as a dynamic baseline rather than a static constraint.
Solution Approach 2:
The system applies preliminary anti-action by pre-establishing safety constraints and boundaries within the behavior generation algorithms. Before allowing personalized driving behaviors to be executed, the system pre-configures safety checklists and mandatory procedure requirements that must be satisfied, preventing harmful deviations before they occur while still permitting comfort-oriented adjustments within safe parameters.
Data Source
AI summary
A method, system and computer-usable medium are disclosed for autonomous vehicle (AV) behavior synchronization. The AV driving pattern is adjusted to facilitate an occupant's satisfaction by receiving information as to person to form a driving history. The driving history is analyzed to identify preferences and patterns. Based on the driving history a driving preference model is formed for the person. AV driving algorithm(s) are adjusted based on the driving preference model for the person when the person is an occupant of the AV.