Neural Network Vehicle Motion Controller for Human-Like Driving
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Solution Overview
Problem
Existing vehicle motion controllers for autonomous and semi-autonomous vehicles produce unnatural and uncomfortable driving behavior by treating lateral and longitudinal motion as separate entities, lacking the human-like dynamics and look-ahead parameters that characterize human driving.
Innovation Solution
A neural network-based vehicle motion controller is trained using 'seat of pants' vehicle dynamics variables and look-ahead parameters to determine steering, throttle, and brake inputs, allowing for human-like navigation by integrating these dynamics and enabling the controller to mimic human driving behavior, including adaptive learning and personality traits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If separate lateral and longitudinal motion controllers are used, then device complexity is reduced, but driving behavior becomes unnatural and uncomfortable
Solution Approach 1:
The patent combines separate lateral and longitudinal motion controllers into a unified neural network-based motion controller. The neural network integrates multiple control functions (lateral steering, longitudinal throttle/brake) into a single system that processes sensor inputs and generates coordinated control outputs, eliminating the robotic feel of separate controllers while maintaining manageable complexity through modular neural network architecture.
Solution Approach 2:
The neural network controller serves multiple functions simultaneously: it performs lateral motion control, longitudinal motion control, and adaptive learning to model human driving behavior. This multi-functional approach replaces multiple specialized controllers with a single universal system that can adapt to different driving styles and conditions.
2Adaptability or versatility
If traditional motion controllers are used, then device complexity is low, but adaptability to different driving styles is poor
Solution Approach 1:
The controller transitions from a static, fixed-logic system to a dynamic, adaptive system. The neural network continuously learns from sensor data and driver behavior patterns, adjusting its internal parameters (weights and biases) to adapt to different driving styles. This dynamic adaptation capability allows the controller to evolve its behavior based on accumulated experience while maintaining a relatively simple underlying architecture.
Solution Approach 2:
The neural network controller performs self-learning and self-adjustment without requiring manual reconfiguration. It automatically models human driving behavior by processing sensor inputs and comparing outputs against recorded human driving patterns, enabling the system to adapt to different driving styles autonomously.
3Measurement precision
If neural network training data is extensive, then modeling precision improves, but loss of time increases during data collection
Solution Approach 1:
The system collects and processes training data in advance during normal vehicle operation. Sensor data from cameras, LIDAR, and other sensors is continuously recorded and used to train the neural network offline, so that when the controller needs to adapt to new driving styles or conditions, the modeling is already complete. This preliminary data collection and processing eliminates the need for time-consuming real-time data gathering.
Data Source
AI summary
A number of variations may include a method of training a neural network vehicle motion controller that more closely replicates how a human would drive a vehicle using seat of pants vehicle dynamics variables and look ahead parameters in order to determine how a motion controller should direct the steering angle, throttle and break inputs to the vehicle to navigate the vehicle.