Undercarriage Sensor Array for Road Anomaly Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in detecting and responding to road anomalies such as traction issues, obstacles, and varying road conditions in real-time, which can impact safety and efficiency.
Innovation Solution
An apparatus comprising a processing unit and sensors positioned on the undercarriage of the vehicle, capable of detecting surface variations and transmitting data to the processing unit for analysis using machine learning algorithms, allowing for adjustments in driving parameters like speed and steering to navigate safely and efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are positioned on the undercarriage to detect surface variations, then measurement precision of road conditions is improved, but device complexity increases
Solution Approach 1:
The sensor system is divided into multiple independent sensors positioned at different locations on the undercarriage, each detecting specific aspects of road surface variations. This segmentation allows the system to achieve comprehensive measurement precision while maintaining modular complexity that can be managed and processed independently.
Solution Approach 2:
The sensor system is designed to perform multiple functions: detecting surface variations, determining traction levels, identifying obstacles, and classifying road conditions. By making the sensor system multi-functional, the patent reduces the need for separate specialized sensors, thereby improving measurement precision across multiple parameters without proportionally increasing device complexity.
2Adaptability or versatility
If machine learning algorithms are used to process sensor information, then adaptability to varying road conditions is improved, but use of energy increases
Solution Approach 1:
Machine learning algorithms are pre-trained offline to recognize patterns of road anomalies and appropriate vehicle responses. During actual operation, the pre-trained models perform inference rather than full learning, which significantly reduces real-time energy consumption while maintaining high adaptability to varying road conditions.
Solution Approach 2:
The system continuously processes sensor information and adjusts vehicle operation in real-time based on detected road conditions. This feedback loop enables the vehicle to adapt to changing conditions dynamically, improving versatility while the energy consumption is managed through efficient algorithm execution triggered only when anomalies are detected.
3Reliability
If real-time detection and response to road anomalies is implemented, then safety is improved, but productivity decreases due to reduced speed
Solution Approach 1:
The system applies full safety measures and speed reductions only when specific road anomalies are detected, rather than maintaining reduced speed continuously. This partial action approach ensures safety when needed while preserving productivity during normal driving conditions, optimizing the trade-off between reliability and productivity.
Solution Approach 2:
The vehicle's operational parameters such as speed and steering are dynamically adjusted based on real-time road condition detection. The system transitions between different operational states (normal cruising, cautious navigation, obstacle avoidance) depending on the detected conditions, thereby maintaining safety during anomalies while preserving productivity during normal conditions.
Data Source
AI summary
An apparatus is provided which includes a processing circuit and a plurality of sensors connected to a vehicle, where at least one of the plurality of sensors is positioned on an undercarriage of the vehicle. The plurality of sensors can detect variations in a road on which the vehicle is traveling. The plurality of sensors can also generate information corresponding to the variations of the road. The plurality of sensors can also transmit the information corresponding to the variations in the road to the processing circuit. The information collected by the plurality of sensors may then be used to augment a driving capability of the vehicle.