Human-Like Vehicle Control for Adaptive Autonomous Driving
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Solution Overview
Problem
Existing autonomous driving technologies fail to replicate human-like driving behaviors, particularly in adapting to real-time situations and passenger preferences, leading to inefficient and unsafe navigation.
Innovation Solution
A system that generates human-like vehicle control signals by combining kinematic models with human-like control models, utilizing self-aware capability parameters and real-time data to adjust vehicle control signals for personalized and adaptive behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional vehicle control systems use generic kinematic models and feedback controllers, then the system structure is simple and easy to implement, but the system cannot adapt to real-time situations and replicate human-like driving behaviors
Solution Approach 1:
The system segments the control architecture into multiple independent modules: perception module for environmental sensing, prediction module for human behavior forecasting, planning module for trajectory generation, and control module for vehicle actuation. Each module operates with specialized functions, allowing the system to achieve human-like adaptability while maintaining manageable complexity through modular design.
Solution Approach 2:
The system introduces intermediary components between the basic controller and the environment: a prediction module that acts as an intermediary to forecast human driver intentions before executing control actions, and a planning module that mediates between predicted intentions and actual vehicle commands. These intermediaries enable adaptive human-like behavior without requiring complete redesign of the control system.
2Adaptability or versatility
If the system incorporates human-like control models with multiple processing modules, then the adaptability to real-time situations improves, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by continuously pre-processing sensor data through the perception module and maintaining updated environmental models. The prediction module pre-calculates probable human intentions based on current state, so when control decisions are needed, the system can quickly select from pre-evaluated options rather than computing from scratch, reducing real-time processing delays.
Solution Approach 2:
The system replaces traditional mechanical feedback controllers with intelligent algorithms including neural networks and probabilistic models. These computational systems process information more efficiently than conventional control mechanisms, enabling complex human-like behavior prediction and adaptation while maintaining fast response times through optimized algorithm execution.
3Productivity
If the system uses traditional route planning based on shortest distance and time, then the navigation efficiency is high, but the system cannot consider passenger preferences and safety considerations
Solution Approach 1:
The route planning system dynamically adjusts navigation strategies based on real-time inputs from passengers and environmental conditions. The planning module continuously re-evaluates routes considering multiple objectives: maintaining navigation efficiency through optimized path selection while adapting to changing passenger preferences, safety requirements, and traffic conditions. This dynamic replanning capability allows the system to balance productivity with adaptability.
Solution Approach 2:
The system implements feedback mechanisms where passenger preferences, comfort levels, and safety concerns are continuously monitored and fed back to the planning module. This feedback loop enables the system to adjust route selections and control parameters in real-time, optimizing both navigation efficiency and passenger satisfaction by incorporating human factors into the automated decision-making process.
Data Source
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AI summary
The present teaching relates to method, system, medium, and implementation of human- like vehicle control for an autonomous vehicle. Information related to a target motion to be achieved by the autonomous vehicle is received, wherein the information includes a current vehicle state of the autonomous vehicle. A first vehicle control signal is generated with respect to the target motion and the given vehicle state in accordance with a vehicle kinematic model. A second vehicle control signal is generated in accordance with a human-like vehicle control model, with respect to the target motion, the given vehicle state, and the first vehicle control signal, wherein the second vehicle control signal modifies the first vehicle control signal to achieve human-like vehicle control behavior.