Ride Comfort Evaluation Model Using Deep Learning and Driving Data
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Solution Overview
Problem
Current methods for evaluating ride comfortability in autonomous driving are cumbersome, inefficient, and subjective, resulting in low universality of the evaluated results due to manual data processing.
Innovation Solution
A data processing method and apparatus that uses a deep learning algorithm model to evaluate ride comfortability by receiving user input data, determining environmental and vehicle parameters, and training a model to output comfortability scores, improving efficiency and objectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data processing is used to evaluate ride comfortability, then the evaluation can be conducted with simple tools, but the processing efficiency is low and the procedure is cumbersome
Solution Approach 1:
The patent replaces the manual mechanical data processing system with an automated computer-based system. The evaluation module automatically collects ride experience information from multiple passengers, processes the data through predetermined algorithms, and generates ride comfortability evaluations without manual intervention, thereby dramatically improving processing efficiency while reducing procedural complexity
Solution Approach 2:
The system enables self-service evaluation by automatically collecting data from passengers during rides, processing it through predefined algorithms, and generating evaluations independently. The computer system serves itself by autonomously completing the entire evaluation workflow from data collection to result generation, eliminating the need for manual data processing operations
2Measurement precision
If manual statistical analysis is conducted on ride experience information, then the evaluation can be performed with basic tools, but the subjectiveness of the evaluation is strong
Solution Approach 1:
The patent replaces subjective manual statistical analysis with objective computer-based algorithmic processing. The evaluation module uses predetermined algorithms to automatically process ride experience information, eliminating human subjectiveness and providing consistent, reproducible evaluation results based on standardized computational methods rather than manual judgment
Solution Approach 2:
The system establishes a universal evaluation framework that can objectively assess ride comfortability across different vehicles, routes, and passenger groups. The standardized algorithmic approach ensures consistent evaluation criteria are applied universally, making the evaluation system adaptable and comparable across diverse scenarios while maintaining objectivity
3Adaptability or versatility
If manual evaluation methods are used, then the implementation is simple, but the universality of the evaluated result is low
Solution Approach 1:
The patent creates a universal evaluation system that can assess ride comfortability across various vehicle types, routes, and operating conditions. The computer-based evaluation module processes diverse ride experience information through standardized algorithms, generating universally comparable results that maintain consistency across different evaluation scenarios while enabling efficient processing of large datasets
Data Source
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AI summary
The invention provides a data processing method, apparatus and readable storage medium for evaluating ride comfortability, by receiving evaluation data input by a user through a data collection port, the evaluation data includes evaluation information of the user for each driving action of a vehicle on which the user rides, determining environmental information and/or vehicle driving parameters when the vehicle executes each driving action, according to the evaluation information corresponding to each driving action of the vehicle, as well as the environmental information and/or vehicle driving parameters, training a preset deep learning algorithm model, to obtain an evaluation model for outputting ride comfortability, the data processing flow for ride comfortability is simplified by establishing an evaluation model that can be configured to output ride comfortability, the processing efficiency is improved; it also makes the evaluation of the obtained ride comfortability more objective, the evaluation model can be adapted to the evaluation of vehicles of various types and various test ride environments, with higher universality.