Vehicle Telemetry Road Classification for Pothole-Aware Lane Control
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Solution Overview
Problem
Existing vehicle systems fail to optimally classify potholes and other road conditions, which can affect automated driving features, leading to suboptimal performance.
Innovation Solution
A system that utilizes sensor data from human-driven vehicles to analyze road conditions, update a map database, and control vehicle systems to avoid or mitigate these conditions, using processors to identify potholes and bumps based on thresholds from sensor data such as vehicle position, steering, and suspension data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing vehicle systems use traditional sensor data analysis methods, then the system complexity remains manageable, but the classification accuracy of potholes and road conditions deteriorates
Solution Approach 1:
The system segments road condition classification into multiple analysis dimensions: lateral control patterns, steering behavior, suspension responses, and vehicle position data. Each dimension is analyzed separately using specific thresholds, then integrated to achieve accurate classification without requiring a single complex analysis system.
Solution Approach 2:
The system dynamically adjusts classification thresholds based on real-time sensor data from human-driven vehicles. Thresholds for lateral control, steering angle, and suspension movement are not fixed but adapt to varying road conditions and driving patterns, enabling accurate classification while maintaining manageable system complexity through rule-based adaptability.
2Measurement precision
If the system collects and analyzes multiple types of sensor data from human-driven vehicles, then the identification accuracy of road conditions improves, but the data processing complexity increases
Solution Approach 1:
Multiple sensor data types (lateral control, steering, suspension, position) are segmented into separate analysis streams, each with dedicated threshold evaluation logic. This modular approach enables accurate multi-dimensional analysis while keeping individual processing tasks simple and manageable.
Solution Approach 2:
The system uses human drivers as unwitting sensors, leveraging their natural driving responses to road conditions as self-generated test data. The drivers' lateral control and steering behaviors automatically provide classification information without requiring active participation or complex instrumentation.
3Reliability
If the system updates the map database in real-time based on sensor data, then the reliability of autonomous navigation improves, but the processing time and computational load increase
Solution Approach 1:
The system pre-establishes threshold values and classification rules for different road conditions before actual operation. This preliminary configuration enables rapid real-time classification without requiring complex computations during critical navigation moments, balancing reliability with processing speed.
Solution Approach 2:
The system processes and stores comprehensive sensor data from all vehicles, even though not all data points are immediately used for navigation. This excessive data collection ensures sufficient information is available for reliable map updates while allowing selective processing to manage computational load.
Data Source
AI summary
Methods and systems are provided for road condition classification that include one or more sensors configured to obtain sensor data pertaining to operation of a vehicle along a roadway by a human; and one or more processors that are coupled to the one or more sensors and that are configured to at least facilitate analyzing the sensor data as to one or more threshold values pertaining to the operation of the vehicle; and identifying one or more conditions of the roadway, based on the analyzing of the sensor data. Further, learned input is provided to the vehicle control system to operate with lane position behavior similar to a human driven vehicle.