Traffic Prediction Using 3D Driving Vectors and Lane Change History
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current traffic prediction systems for vehicles lack accuracy in detecting optimal routes and predicting traffic volume, often relying on outdated methods that do not account for real-time vehicle data and lane changes, leading to inefficient navigation and increased congestion.
Innovation Solution
A traffic prediction system that includes a vehicle-mounted display apparatus and a server apparatus, which calculate driving probabilities using lane change history, driving vectors, and 3D coordinates to predict traffic volume, allowing vehicles to transmit and receive data for optimized route planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional traffic prediction systems are used, then the system structure is simple, but the measurement precision of traffic volume prediction is poor
Solution Approach 1:
The system segments traffic prediction into multiple components: lane change detection, driving vector calculation, and traffic volume prediction. Each component processes specific data independently before integration, improving prediction accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system transitions from traditional 2D map-based navigation to 3D spatial coordinates for vehicle positioning and route prediction. By incorporating vertical dimension (overpasses, underpasses) and precise spatial vectors, the system achieves more accurate traffic volume prediction without excessive complexity increase.
2Measurement precision
If real-time vehicle data and lane change information are incorporated, then the traffic prediction accuracy is improved, but the device complexity increases
Solution Approach 1:
The server apparatus performs multiple functions: receiving vehicle position data, calculating driving vectors, detecting lane changes, and predicting traffic volume. This multi-functional approach consolidates complexity into a centralized system while providing accurate real-time predictions without requiring complex distributed processing.
Solution Approach 2:
The system continuously receives real-time vehicle position data and lane change information from multiple vehicles, processes this feedback data to update driving vectors and probabilities, and adjusts traffic volume predictions dynamically. This feedback mechanism improves accuracy while managing complexity through iterative refinement rather than complex static models.
3Measurement precision
If driving probability calculation using lane change history is implemented, then the route detection accuracy is improved, but the loss of time for data processing increases
Solution Approach 1:
The system pre-calculates driving vectors from historical lane change data and stores them for quick retrieval. When predicting traffic volume, the system uses these pre-computed vectors and probabilities rather than calculating everything in real-time, thereby improving route detection accuracy while minimizing processing time delays.
4Measurement precision
If 3D map information and multiple position acquisitions are used, then the driving vector calculation accuracy is improved, but the loss of time for data acquisition increases
Solution Approach 1:
The system continuously acquires vehicle position data at multiple time points along the driving path, maintaining continuous tracking rather than periodic sampling. This continuous data stream improves driving vector calculation accuracy by capturing subtle position changes while minimizing time loss through efficient real-time processing of the continuous stream.
Data Source
AI summary
Disclosed herein are a traffic prediction system, a vehicle-mounted display apparatus, a vehicle, and a traffic prediction method. The traffic prediction system includes: a vehicle configured to acquire a driving probability for at least one drivable route; and a server apparatus configured to receive the driving probability for the at least one drivable route from the vehicle, and to calculate a volume of traffic for the at least one drivable route based on the driving probability for the at least one drivable route.