Vehicle Vision Traffic Prediction for Lane-Level Jam Probability
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Solution Overview
Problem
Existing vehicle imaging systems lack the ability to accurately predict traffic jam probabilities, leading to inefficiencies and frustration for drivers, particularly in metropolitan areas where traffic congestion is prevalent.
Innovation Solution
A vehicle vision system utilizing CMOS cameras, radar sensors, and lidar sensors to capture data, process it with an ECU, and determine traffic attributes using a trained prediction model to calculate lane-by-lane traffic jam probabilities, incorporating human-annotated synthetic data and linear regression techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle imaging systems use basic object detection only, then the system complexity is low, but the ability to predict traffic jam probabilities is insufficient
Solution Approach 1:
The system segments traffic analysis into multiple components: basic object detection, traffic attribute determination (density, flow rate, velocity), and predictive modeling. Each component processes specific aspects of traffic data independently, allowing the system to achieve comprehensive traffic jam probability prediction while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by collecting and processing traffic attributes (density, flow rate, collective velocity) in advance before predicting traffic jam probabilities. This preliminary data preparation and analysis enables more accurate predictions without requiring complex real-time computation during critical decision-making moments.
2Measurement precision
If the system processes real-time sensor data through trained prediction models, then traffic jam probability prediction accuracy improves, but computational energy consumption increases
Solution Approach 1:
The system applies partial action by selectively processing only the most relevant traffic attributes (density, flow rate, collective velocity) through the prediction model rather than analyzing all possible sensor data. This selective processing maintains prediction accuracy while reducing unnecessary computational energy consumption.
3Loss of information
If the system provides detailed lane-by-lane traffic predictions, then driver information completeness improves, but information processing complexity increases
Solution Approach 1:
The system segments traffic information by lane, providing separate traffic jam probability predictions for each lane. This segmentation delivers comprehensive lane-by-lane information to drivers while simplifying the presentation of complex data through organized, lane-specific displays rather than overwhelming aggregated information.
Data Source
AI summary
A vehicular driving assist system includes at least one sensor disposed at a vehicle and having a field of sensing exterior of the vehicle. An ECU includes circuitry and associated software, with the circuitry including a data processor for processing sensor data captured by the sensor to detect presence of objects in the field of sensing of the sensor. The ECU, responsive to processing by the data processor at the ECU of sensor data captured by the sensor, determines traffic attributes for a plurality of traffic lanes of a road the vehicle is travelling along. The ECU, responsive to determining the traffic attributes determines a predicted traffic value based on the traffic attributes and an output from a trained prediction model. The ECU, responsive to determining the predicted value, determines a traffic jam probability for at least one traffic lane based on the predicted value and the respective traffic attributes.