Rail Vehicle Driving Assistance via Recorded Driver Behavior
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Solution Overview
Problem
Current driving assistance systems for vehicles, such as trains, are inefficient in reducing energy consumption as they do not account for the specificities of a journey, requiring extensive data measurement and complex simulations, leading to increased energy use, cost, and wear on vehicle components.
Innovation Solution
A method that involves an acquisition phase to determine and store driving instructions based on actual driver behavior, allowing for assistance phase guidance using stored data on vehicle location and energy consumption, enabling energy-efficient driving without the need for extensive data measurement or complex simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If simulation-based driving assistance systems are used to account for journey specificities, then energy consumption is reduced, but device complexity and setup cost increase
Solution Approach 1:
The patent creates a digital copy of optimal driving behavior by recording actual driver actions (acceleration, braking, throttling) and their corresponding energy consumption outcomes. This copied behavioral data is stored and reused for similar journey conditions, eliminating the need for complex simulation systems while achieving energy efficiency through proven human driving patterns.
Solution Approach 2:
The system enables drivers to contribute to the knowledge base through their own driving actions. Each driver's behavior is automatically recorded and added to the collective experience repository, allowing the system to self-improve and expand its library of optimal driving patterns without requiring external data collection infrastructure.
2Measurement precision
If extensive data measurement and simulation are performed to account for journey specificities, then driving assistance accuracy is improved, but loss of time and implementation cost increase
Solution Approach 1:
The system performs preliminary data collection during normal driving operations, continuously building the knowledge base in the background. By the time specific journey conditions are encountered, the optimal driving instructions have already been recorded and are immediately available, eliminating the need for time-consuming pre-trip setup or simulation processes.
Solution Approach 2:
The system implements continuous feedback loops where driving instructions are issued, executed, and their energy consumption outcomes are measured and stored. This feedback mechanism automatically refines the knowledge base with real-world performance data, improving accuracy over time without requiring manual intervention or extensive measurement campaigns.
3Ease of operation
If speed regulator systems are used to maintain predetermined speed, then speed control is simplified, but energy consumption increases due to ignoring journey specificities
Solution Approach 1:
The system transitions from static speed maintenance to dynamic speed optimization by selecting driving instructions based on current journey conditions matching historical patterns. The recommended instructions dynamically adjust acceleration, braking, and throttling actions based on the specific route, terrain, and traffic conditions encountered, achieving energy efficiency while maintaining operational simplicity.
Data Source
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AI summary
The method involves iterating an acquisition phase (100). A control instruction is indicated (106) to a vehicle by a driver to meet a predetermined speed. Energy consumption of the vehicle to carry out the control instruction is determined (118). The vehicle at the time of indication consigns is localized. An indication of the control instruction in an assistance phase is stored for driving function. The location of the vehicle is localized with respect to nature and the localization of the control instruction. An independent claim is also included for a system for assisting driving of a vehicle.