Vehicle Actuator Remapping for Driver Behavior Optimization
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Solution Overview
Problem
Existing driver management systems are inadequate in improving fuel economy and reducing emissions, as they are punitive, simplistic, or unable to adapt to multiple drivers and dynamic driving behaviors.
Innovation Solution
A system, method, and apparatus that identifies and defines traits of good drivers to optimize vehicle performance by interpreting condition data, determining operating parameters, and remapping actuator responses to match good driver profiles, thereby improving fuel economy and reducing emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If punitive control schemes are used to limit torque or speed in response to suboptimal driver performance, then fuel economy and emissions are improved, but driver behavior cannot be fundamentally changed and the system cannot adapt to multiple drivers
Solution Approach 1:
The system dynamically adapts control parameters based on identified driver behavior patterns rather than using fixed punitive limits. The control system transitions from static torque/speed limits to dynamic adjustments that respond to real-time driver actions and learned behavior models, allowing adaptation to multiple drivers with different driving styles.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring driver performance, comparing it against optimal behavior models, and adjusting control parameters accordingly. This feedback mechanism enables the system to learn from driver responses and adapt control strategies over time, improving both fuel economy and multi-driver adaptability.
2Ease of operation
If simple speed or power adjustments are provided in cruise control, then driver performance is managed, but the system cannot help poor drivers imitate the behaviors of good drivers
Solution Approach 1:
The system creates virtual models of optimal driver behavior by analyzing data from skilled drivers. These behavioral models serve as templates that the control system uses to guide less skilled drivers, effectively copying successful driving patterns and applying them through automated control adjustments to improve fuel economy.
Solution Approach 2:
The system replaces traditional mechanical cruise control with an intelligent control system that uses sensor data, processing, and automated actuator control. This substitution enables sophisticated behavior analysis and real-time adjustments that go beyond simple speed maintenance, allowing the system to actively shape driver behavior toward optimal patterns.
3Reliability
If fixed response penalties are applied to poor driver performance, then suboptimal behavior is punished, but the system cannot adjust response time intelligently or affect dynamic vehicle behavior
Solution Approach 1:
The system replaces fixed response penalties with dynamic control adjustments that vary based on the specific driving situation, driver behavior patterns, and vehicle state. Response timing and magnitude are continuously adapted based on real-time analysis, enabling intelligent adjustment rather than rigid predetermined penalties.
Solution Approach 2:
The system changes multiple control parameters simultaneously (torque, speed, acceleration rates) rather than applying single fixed penalties. These parameter adjustments are dynamically modified based on the degree of suboptimal behavior, environmental conditions, and learned driver patterns, enabling nuanced and adaptive control responses.
4Loss of energy
If maximum value limits are imposed on torque or speed, then driver performance is constrained, but the system cannot affect transient behavior where efficiency losses occur
Solution Approach 1:
The system focuses control efforts on transient phases (acceleration, deceleration, gear shifts) where efficiency losses occur, rather than imposing continuous maximum value limits. Dynamic control strategies are applied specifically during these transient events to optimize energy efficiency while maintaining simplicity during steady-state operation.
Solution Approach 2:
The system prepares control adjustments in advance of anticipated transient events by monitoring driver intent and vehicle state. Control parameters are pre-adjusted before efficiency-critical transitions occur, allowing the system to proactively manage energy losses during acceleration and deceleration events rather than reactively limiting maximum values.
Data Source
AI summary
An apparatus includes a control circuit. The control circuit is structured to interpret condition data indicative of an external operating condition of a vehicle, determine an operating parameter of the vehicle based on an actuator response of an actuator of the vehicle, compare the operating parameter to an operating parameter threshold where the operating parameter threshold is based on the external operating condition, and remap an actuator response map of the actuator based on the comparison indicating that the operating parameter does not satisfy the operating parameter threshold. The operating parameter includes at least one of a fuel economy value, an emissions value, an acceleration value, a braking value, or a wear value.