Edge-Based Road Anomaly Detection for Adaptive Vehicle Speed
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Solution Overview
Problem
Current Intelligent Speed Adaptation (ISA) systems are limited in their application contexts and do not effectively extend beyond traditional speed limit enforcement, failing to address anomalous situations on roads that may require adaptive speed control.
Innovation Solution
A system that utilizes a proprietary recognition algorithm and Multi-access Edge Computing (MEC) technology to identify anomalous situations along roads, processing data from vehicles and roadside edge nodes to adjust driving speeds in real-time, incorporating a hybrid processing architecture for enhanced accuracy and latency reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional speed limit enforcement systems are used, then the system is simple and easy to operate, but the adaptability to anomalous situations is limited
Solution Approach 1:
The system performs multiple functions: it detects both standard speed limit signs and anomalous situations (accidents, obstacles, traffic conditions), and provides both information gathering and speed control functions. This multi-functionality allows the system to adapt to various road conditions beyond traditional speed enforcement.
Solution Approach 2:
The system divides the road environment into discrete detectable elements (speed limit signs, anomalies, road conditions) and processes them separately through different detection mechanisms, allowing complex adaptability while maintaining manageable system architecture.
2Measurement precision
If real-time data processing from multiple vehicles is implemented, then the measurement precision of anomalous situations improves, but the computational complexity and processing time increase
Solution Approach 1:
The system combines data from multiple vehicles and roadside edge nodes into a unified detection framework. By merging multiple data sources and their respective detections, the system achieves higher measurement precision through data correlation and validation across different observers.
Solution Approach 2:
Roadside edge nodes act as intermediaries that pre-process and validate data before it reaches the central system. This intermediary layer reduces the computational burden on the main system while maintaining high detection accuracy through distributed processing.
3Speed
If roadside edge nodes are deployed for data processing, then the processing speed and response time improve, but the infrastructure complexity and cost increase
Solution Approach 1:
Roadside edge nodes perform preliminary data processing, filtering, and validation before data is transmitted to the central system. This pre-processing action at the edge reduces the volume of data requiring central processing and enables faster local responses to critical situations.
Solution Approach 2:
The system adds a spatial dimension to data processing by distributing computational resources across multiple locations (roadside edge nodes) rather than relying solely on centralized processing. This dimensional expansion of the processing architecture enables parallel operations and reduced latency.
Data Source
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AI summary
System (1) for automatically recognising anomalous situations along roads travelled by motor-vehicles for intelligent motor-vehicle driving speed control (2) along roads. The motor-vehicles (2) are configured to transmit data allowing anomalous situations to be recognised along roads travelled by the motor-vehicles (2). The system (1) comprises data processing resources (3) configured to: - receive and process data transmitted by the motor-vehicles (2) to recognise anomalous situations along the roads travelled by the motor-vehicles (2) based on a recognition algorithm, - when anomalous situations are recognised along roads travelled by the motor- vehicles (2), generate associated alert events and compute reference driving speeds along the roads recognised to be affected by anomalous situations, and - transmit data representative of the alert events and of the reference driving speeds along the roads recognised to be affected by anomalous situations. The motor-vehicles (2) are further configured to: - receive data representative of alert events and reference driving speeds, and - use the received data to implement one or both of the following actions: ° inform the drivers of motor-vehicles (2), through automotive user interfaces of motor-vehicles (2) of the anomalous situations recognised along roads travelled by motor-vehicles (2), and ° cause current driving speeds of the motor-vehicles (2) to be adjusted to the reference driving speeds along roads recognised to be affected by anomalous situations.