Autonomous Vehicle Lane Planning With Human-Like Driving Models
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Solution Overview
Problem
Current autonomous driving technologies fail to replicate human-like driving behaviors, particularly in lane planning and control, as they do not account for diverse driving preferences and real-time situational adaptations.
Innovation Solution
A system and method for autonomous vehicles that utilize sensor data and self-aware capability parameters, generated from recorded human driving data, to plan and control lane behavior in a human-like manner, adapting to real-time situations and passenger preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional autonomous driving planning modules use generic vehicle kinematic models and feedback controllers, then the system structure remains simple, but the driving behavior fails to replicate human-like lane control adaptations
Solution Approach 1:
The patent copies human driving behavior patterns by training a neural network model on recorded human driving data. The model learns and replicates how human drivers adjust lane positioning based on vehicle type, road conditions, and turning scenarios, enabling autonomous vehicles to mimic natural human-like lane control without requiring complex explicit programming rules
Solution Approach 2:
The patent replaces traditional mechanical control systems (generic kinematic models and feedback controllers) with an intelligent system based on neural networks and machine learning. This substitution allows the system to adaptively learn human driving patterns from data, achieving human-like behavior through computational intelligence rather than rigid mechanical control logic
2Adaptability or versatility
If autonomous driving systems use conventional route planning based on shortest distance and traffic patterns, then computational efficiency is maintained, but diverse driving preferences and real-time situational adaptations are not accounted for
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network model offline using extensive recorded human driving data. This pre-training phase captures diverse driving preferences and behavioral patterns in advance, so that during real-time autonomous operation, the system can quickly query pre-learned behaviors without performing complex computations, thus maintaining both adaptability and computational efficiency
Data Source
AI summary
The present teaching relates to method, system, medium, and implementation of lane planning in an autonomous vehicle. Sensor data are received that capture ground images of a road the autonomous vehicle is on. Based on the sensor data, a current lane of the road that autonomous vehicle is currently occupying is detected. Lane control for the autonomous vehicle is planned based on the detected current lane and self-aware capability parameters in accordance with a driving lane control model. The self-aware capability parameters are used to predict operational capability of the autonomous vehicle with respect to a current location of the autonomous vehicle. The driving lane control model is generated based on recorded human driving data to achieve human-like lane control behavior in different scenarios.