Intersection Speed Guidance Using Traffic-Light-Aware Deep Learning
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Solution Overview
Problem
Drivers struggle to determine optimal approaching speeds at intersections due to uncertainty about traffic light statuses ahead, leading to increased stops, oil consumption, and environmental pollution.
Innovation Solution
A system utilizing a deep learning module with a neural network simulating human brain capabilities to analyze traffic and environment data, generating a control model that computes a recommended vehicle speed to avoid intersection stops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If drivers blindly adopt an approaching speed without knowing traffic light status, then the system is simple and easy to operate, but the number of stops at intersections increases
Solution Approach 1:
The patent introduces an intermediary system consisting of road-side units and a server that mediates between traffic lights and drivers. The road-side units collect traffic light status and transmit it to the server, which then provides recommended speeds to drivers via mobile devices, enabling drivers to make informed speed decisions without directly observing complex traffic light patterns
Solution Approach 2:
The system performs preliminary action by calculating and providing recommended approaching speeds to drivers before they reach the intersection. The server computes optimal speeds based on current traffic light status and road conditions, and transmits these recommendations to drivers in advance, allowing drivers to adjust their speed proactively rather than reacting to stops
2Reliability
If drivers stop more frequently at intersections due to uncertainty, then safety may improve, but oil consumption and toxic emissions increase
Solution Approach 1:
The patent implements feedback by continuously monitoring traffic light status, vehicle position, and road conditions, then using this information to dynamically calculate and update recommended speeds. The system provides real-time feedback to drivers through mobile devices, enabling them to maintain optimal speeds that prevent stops and reduce emissions while ensuring safe intersection passage
Solution Approach 2:
The patent replaces the mechanical system of physical observation and manual speed adjustment with an electronic information system. Instead of drivers visually monitoring traffic lights and manually adjusting speeds, the system uses electronic communication to transmit traffic status and computationally determined recommended speeds directly to drivers, reducing the need for stops and associated emissions
3Productivity
If a deep learning system is implemented to determine optimal speed, then the number of stops decreases, but the device complexity increases
Solution Approach 1:
The patent segments the speed determination system into distinct functional modules: road-side units for data collection, a server for deep learning computation and model training, and mobile devices for presenting recommendations to drivers. This segmentation allows the complex deep learning functionality to be concentrated in the server while keeping individual components relatively simple and manageable
Data Source
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AI summary
A system and method for determining a vehicle speed, the system comprises: a first collecting module, adapted to collect traffic information; a deep learning module, comprising a control model and a neural network, the control model is generated after the neural network autonomously deeply learns based on inputted traffic information; the control model is adapted to receive current traffic information comprising a peed and a location of a vehicle, traffic light status at a junction ahead, and traffic condition information in a road segment ahead currently collected by the first collecting module, compute a recommended vehicle speed for the vehicle based on the current traffic information, and output the recommended vehicle speed. The technical solutions of the system may reduce the times of stops at the intersection.