Vehicle Engine Control via Traffic Prediction
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Solution Overview
Problem
Existing vehicle engine control systems do not effectively utilize visual data from cameras to predict and manage traffic congestion, leading to inefficient fuel consumption and increased exhaust emissions due to prolonged idling.
Innovation Solution
A system equipped with a camera and controller that uses statistical models to determine the probability of traffic congestion and automatically stop or restart the engine based on predefined thresholds, reducing idling time and improving fuel economy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the engine is stopped to reduce fuel consumption, then fuel economy is improved, but the vehicle cannot respond quickly when traffic moves
Solution Approach 1:
The system performs preliminary actions by predicting traffic congestion before it occurs and stopping the engine in advance. The statistical models analyze camera data to forecast congestion, allowing the engine to be stopped proactively rather than reactively, reducing fuel consumption while maintaining readiness to restart when traffic moves.
Solution Approach 2:
The system continuously monitors traffic conditions using camera data and statistical models, creating a feedback loop that adjusts engine operation based on predicted traffic flow. This feedback mechanism ensures the engine is stopped only when congestion is likely, maintaining responsiveness while optimizing fuel economy.
2Object-generated harmful factors
If the engine is stopped to reduce exhaust emissions, then emission rate is reduced, but driver convenience deteriorates
Solution Approach 1:
The system stops the engine preliminarily based on predicted congestion before the vehicle actually stops, rather than waiting for the vehicle to stop first. This proactive approach reduces emissions while minimizing the impact on driver convenience by maintaining a more natural driving experience.
Solution Approach 2:
The system changes the operational parameters of the engine by introducing probability thresholds and time-based criteria. The statistical models evaluate multiple parameters (traffic density, movement patterns, time duration) to determine when engine shutdown is appropriate, balancing emission reduction with driver convenience through data-driven decision-making.
3Use of energy by moving object
If statistical models are used to predict traffic congestion, then fuel economy is improved, but device complexity increases
Solution Approach 1:
The controller performs multiple functions: it controls engine operation, processes camera data, runs statistical models, and makes shutdown decisions. By consolidating these functions in a single controller rather than adding separate dedicated systems, the patent reduces overall device complexity while achieving improved fuel economy through intelligent prediction.
Solution Approach 2:
The statistical models use data from the existing camera system to self-determine when engine shutdown is appropriate, without requiring additional sensors or complex external systems. The system serves itself by utilizing already-available visual data and applying statistical analysis to make autonomous engine control decisions.
Data Source
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AI summary
A system for controlling an engine of a vehicle. In one embodiment, the system includes at least one monitoring device mounted on the vehicle, a controller in electronic communication with the at least one monitoring device, and a computer readable memory storing instructions executed by the controller. The instructions cause the controller to determine a current driving path of the vehicle based on data received from the at least one monitoring device, to detect a traffic congestion ahead of the vehicle in the current driving path based on data received from the at least one monitoring device, and to determine an alternative driving path of the vehicle based on data received from the at least one monitoring device. The instructions further cause the controller to calculate, using a first statistical model, a first probability that the traffic congestion will not move within a defined time period, and to stop the engine before the vehicle comes to a full stop when the first probability is greater than a first threshold.