Mobile LiDAR Platform for Dynamic Speed Enforcement
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Solution Overview
Problem
Current speed enforcement methods are inadequate in dynamically measuring and enforcing speed limits for vehicles in motion, particularly failing to effectively detect excessive speeds and tailgating from a moving patrol car.
Innovation Solution
A mobile LiDAR platform equipped with a LiDAR sensor, camera, and computing node uses Machine Learning and Deep Learning algorithms to project point clouds onto a plane, detect bounding boxes of vehicles, calculate relative and absolute speeds, and detect tailgating by integrating GPS data and applying rules like the three-second headway rule.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mobile LiDAR platform is used for speed enforcement, then speed measurement capability is improved, but device complexity increases
Solution Approach 1:
The system segments the speed enforcement function into multiple independent modules: LiDAR sensor for point cloud acquisition, GPS receiver for reference speed, convolutional neural network for vehicle detection, and processing logic for speed calculation. Each module performs a specific task, making the complex system manageable and maintainable while achieving accurate speed measurement.
Solution Approach 2:
The mobile LiDAR platform integrates multiple functions into a single system: vehicle detection, speed measurement, GPS tracking, and tailgating detection. The LiDAR sensor serves both as a detection tool for vehicle presence and as a measurement tool for speed calculation, reducing the need for separate dedicated devices.
2Measurement precision
If Deep Learning algorithms are used for vehicle detection, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-training the convolutional neural network model offline with large datasets. The pre-trained model can then quickly process real-time LiDAR point clouds during actual speed enforcement operations, reducing processing time while maintaining high detection accuracy.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based vehicle detection methods with Deep Learning-based convolutional neural networks. This substitution enables more accurate detection of vehicles in complex scenarios (occlusions, varying lighting, different vehicle types) while the system optimizes the neural network for real-time processing efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and dynamic speed enforcement by determining absolute vehicle speeds and detecting tailgating from a moving patrol car, enhancing safety by effectively policing speed limits and reducing fatalities.
Implementation Method 1
a LiDAR sensor... measure the relative velocity of the surrounding vehicles
Implementation Method 2
a LiDAR sensor... measure the relative velocity of the surrounding vehicles
Data Source
AI summary
Mobile LiDAR platforms for vehicle tracking are provided. In various embodiments, a time-series of point clouds is received from a LiDAR sensor. Each point cloud of the time series of point clouds is projected onto a plane. The projected point clouds are provided to a convolutional neural network. At least one bounding box of a vehicle within each of the projected point clouds is received from the convolutional neural network. A relative speed of the vehicle is determined from the bounding boxes with the projected point clouds. A reference speed of the LiDAR sensor is determined from the GPS receiver. From the relative speed and the reference speed, an absolute speed of the vehicle is determined.