Road Feature Detection via Vehicle Motion Profiles
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Solution Overview
Problem
Current methods for detecting and characterizing road features, such as potholes and speed bumps, are unreliable and labor-intensive, relying heavily on human input and are prone to inconsistency and bias, making it difficult to effectively identify and map road features across large areas.
Innovation Solution
A trained statistical model, such as a machine learning model, is used to analyze vehicle motion profiles to identify road features and characteristics, allowing for automated detection and mapping of road features, which can be used for proactive vehicle control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human-based methods are used to detect and characterize road features, then detection can be performed, but the methods are unreliable, labor-intensive, and prone to inconsistency and bias
Solution Approach 1:
The patent replaces manual human inspection and characterization of road features with an automated system using vehicle motion sensors and machine learning algorithms. The system captures vehicle motion data (acceleration, velocity, position) and uses trained statistical models to automatically detect and characterize road features such as potholes, speed bumps, and manhole covers, eliminating the need for human labor while improving consistency and reliability.
Solution Approach 2:
The system enables vehicles to automatically detect and characterize road features through their own motion data without requiring external human intervention. The vehicle's existing motion sensors serve the dual purpose of vehicle control and road feature detection, allowing the system to self-service the detection task using data already being collected during normal vehicle operation.
2Productivity
If automated vehicle motion profile analysis is used to identify road features, then labor costs and human error are reduced, but the system requires training data collection and model development
Solution Approach 1:
The patent implements a two-stage process where training data is collected and models are developed in advance before deployment. During the preliminary phase, vehicle motion profiles are collected from various road conditions and used to train statistical models. Once trained, the models are deployed for efficient real-time detection, separating the complex model development phase from the efficient detection phase.
Solution Approach 2:
The system uses universal vehicle motion sensors that serve multiple functions: primary vehicle control and navigation, and secondary road feature detection and characterization. The same sensors and data collection infrastructure used for vehicle operation are repurposed for road feature detection, reducing the need for dedicated detection hardware and simplifying the overall system.
3Measurement precision
If statistical models are trained using vehicle motion profiles from multiple vehicles, then detection accuracy improves, but data collection and processing time increases
Solution Approach 1:
The patent combines vehicle motion profiles from multiple vehicles to create a comprehensive training dataset. By merging data from different vehicles, road types, and conditions, the system creates a more robust and accurate statistical model that generalizes better across diverse scenarios. The combined dataset accelerates model convergence and improves detection accuracy for various road feature types.
Solution Approach 2:
The system uses a sufficient number of training samples to achieve adequate model accuracy without requiring exhaustive data collection. By selecting representative motion profiles from multiple vehicles and road conditions, the system achieves good detection performance with a practical amount of training data, balancing accuracy requirements with training time constraints.
Data Source
AI summary
Methods and systems for generating a map of road features are provided. A method may include obtaining a vehicle motion profile, inputting the vehicle motion profile to a trained statistical model, and outputting one or more road features from the trained statistical model. A method may include obtaining first vehicle motion profiles, obtaining second vehicle motion profiles, generating a trained statistical model using the first vehicle motion vehicle motion profiles and the second vehicle motion profiles, and storing the trained statistical model in non-volatile computer readable memory.