Driver Signature-Based Vehicle Tuning for Fleet Efficiency
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Solution Overview
Problem
Current automotive systems lack the ability to consistently maintain efficiency and comfort across different vehicles due to varying driving behaviors and vehicle dynamics, leading to inefficient fuel usage and discomfort, especially in autonomous driving systems which do not account for individual driver preferences.
Innovation Solution
The development of a driver signature system that predicts upcoming driver actions through machine learning algorithms using sensor data, allowing vehicles to adjust components such as shock absorbers and engine settings to match the driver's behavior, ensuring consistent performance and comfort across different vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If driver behavior is not correlated with vehicle dynamics, then fleet efficiency varies significantly across different vehicles, but implementing behavior correlation requires complex driver signature analysis and real-time vehicle tuning systems
Solution Approach 1:
The system performs preliminary driver behavior analysis by creating driver signatures from historical sensor data before actual driving occurs. This allows the vehicle to pre-adjust its dynamics and control characteristics to match the driver's preferred behavior pattern, eliminating the need for complex real-time adjustments during driving while maintaining consistent fleet efficiency.
Solution Approach 2:
The system creates a digital copy of driver behavior through the driver signature, which replicates the driver's preferred vehicle dynamics, acceleration patterns, and control characteristics. This copy is then applied to tune the vehicle's control systems, allowing consistent driver-vehicle interaction across the entire fleet without requiring physical modifications to each vehicle.
2Ease of operation
If autonomous driving systems use generic control algorithms, then development is simpler and faster, but individual driver preferences and comfort are not accounted for
Solution Approach 1:
The system applies local quality by customizing vehicle control characteristics specifically for each driver's preferences. Instead of using a single generic control algorithm for all drivers, the system adjusts suspension, steering, and powertrain control parameters locally for each driver based on their signature, providing personalized comfort while maintaining overall system simplicity through modular architecture.
3Use of energy by moving object
If vehicle components are not tuned to driver behavior, then vehicle dynamics remain consistent across all drivers, but fuel efficiency and ride comfort vary significantly
Solution Approach 1:
The system optimizes fuel efficiency by dynamically changing vehicle operating parameters such as engine torque curves, transmission shift points, and regenerative braking thresholds based on the driver's behavior signature. This allows the vehicle to operate at optimal efficiency points for each driver's driving style without requiring complex mechanical modifications, achieving better fuel economy while maintaining consistent vehicle dynamics across the fleet.
Data Source
AI summary
The driver's driving behavior will be recorded and a signature will be created either on vehicle or on a cloud data center. The driver signature can be accessed using secure authentication by any vehicle he will be driving. The data collection is a continuous process monitoring driving behavior of the driver. The guidelines from OEM are used to compare driver signature with ideal driving characteristics. Changes are pushed to the vehicle controller to modify controls of individual automotive components to adjust the efficiency of vehicle and improve the ride comfort for the driver. The changes to be made can be decided on a remote cloud system or on the vehicle.