Roadwork Zone Prediction Using Map and Multi-Vehicle Sensor Data

The system creates a universal prediction model that serves multiple functions: it processes sensor data from various vehicle types, handles different sensor configurations, and provides predictions for multiple vehicles simultaneously. The machine learning model is designed to be agnostic to specific sensor implementations, allowing the same system to serve diverse autonomous vehicle platforms with varying sensor suites.

The system introduces a centralized server as an intermediary that receives sensor data from multiple vehicles, processes it through machine learning models, and distributes predictions back to vehicles. This intermediary architecture simplifies individual vehicle systems by offloading the complex data aggregation and model training tasks to a centralized infrastructure, reducing on-vehicle computational requirements.

The system performs preliminary data processing and model training in advance using historical sensor data and ground truth data. By pre-training machine learning models with extensive datasets and pre-processing map data for relevant road segments, the system reduces real-time processing requirements. When a vehicle needs a prediction, the heavy computational work has already been completed, and only inference is required.

The system focuses data collection and processing efforts on local road segments that are relevant to each vehicle's current location and route. Rather than processing all available data globally, the system identifies and processes only the sensor data and map data pertinent to specific road segments ahead of the vehicle's path, reducing unnecessary computational overhead while maintaining prediction accuracy for relevant areas.