Vehicle Longitudinal Guidance Model Using Route Segmentation
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Solution Overview
Problem
Current methods for determining predicted longitudinal guidance of vehicles do not accurately account for driver-specific behavior and varying road conditions, leading to suboptimal energy consumption and travel time predictions.
Innovation Solution
A method and system that divide a vehicle's route into sections based on specific types, using sensors and map data to detect actual speed-time profiles, which are then adapted to determine expected speed-time profiles by incorporating driver-specific and environmental parameters, allowing for precise modeling of longitudinal guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If driver-specific behavior and environmental parameters are incorporated into the model, then prediction accuracy of longitudinal guidance is improved, but model complexity and computational requirements increase
Solution Approach 1:
The route is divided into multiple sections with different section types (e.g., urban, rural, highway), and separate reference speed-time profiles are created for each section type. This segmentation allows the model to capture driver-specific behavior and environmental conditions in a structured manner, improving prediction accuracy while managing complexity through modular organization of parameters and data structures.
2Use of energy by moving object
If actual speed-time profiles are detected and used to adapt reference profiles, then energy consumption prediction is optimized, but data processing requirements and system resource usage increase
Solution Approach 1:
The system detects and processes only the necessary portions of speed-time profile data required for adapting reference profiles, rather than processing all possible vehicle data. By focusing on partial data sets that are most relevant for energy consumption prediction (such as acceleration patterns, speed variations, and section-specific driving behavior), the system optimizes energy prediction while minimizing data processing requirements and resource usage.
3Manufacturing precision
If route is divided into multiple sections with different types, then longitudinal guidance precision is improved, but system complexity and processing time increase
Solution Approach 1:
Reference speed-time profiles are pre-calculated and stored for different section types before actual route processing. When a route is divided into sections, the system can directly retrieve and adapt these pre-computed reference profiles rather than calculating them in real-time. This preliminary preparation significantly reduces processing time during actual longitudinal guidance while maintaining high precision through section-specific adaptations.
Data Source
AI summary
A method to detect a section of a route of the vehicle using sensors, an actual speed-time profile when driving through the section, and parameters based on the actual speed-time profile. The method includes determining a route of the vehicle, wherein the route runs from a current position of the vehicle to a target position of the route; dividing the route into sections, wherein each of the sections is assigned to a predetermined section type and is assigned a reference speed-time profile which is dependent on the section type of the respective section; determining an expected speed-time profile for each of the sections in that the reference speed-time profile of the respective section is provided with parameters which are dependent on a current driver to determine the expected speed-time profile of the respective section; and determining the longitudinal guidance using the expected speed-time profiles of the sections of the route.