Traffic Signal Navigation With Predictive Lane Selection
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Solution Overview
Problem
Existing navigation systems fail to provide accurate lane selection and turn decisions at traffic lights, particularly in regions with varying driver habits, leading to inefficiencies and increased travel times.
Innovation Solution
A vehicle system utilizing sensors and a controller to detect traffic signals, count vehicles, determine vehicle types, calculate lane acceleration profiles, and compare them to select the optimal lane based on sensor information and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If navigation systems provide basic route guidance, then drivers receive turn and lane information, but travel time efficiency is reduced due to inability to optimize for traffic signal conditions and regional driving habits
Solution Approach 1:
The system performs preliminary analysis of traffic signal patterns, vehicle queue compositions, and regional driving habits before the vehicle reaches the intersection. By pre-calculating optimal lane choices based on detected red lights and projected vehicle movements, the system provides advance guidance that reduces actual travel time through the intersection.
Solution Approach 2:
The system continuously monitors traffic conditions, vehicle types in each lane, and signal patterns, then adjusts lane recommendations in real-time. This feedback loop allows the system to adapt to changing conditions and regional driving patterns, improving both time efficiency and adaptability simultaneously.
2Measurement precision
If the system analyzes multiple lanes and vehicle types to determine optimal lane selection, then navigation accuracy improves, but system complexity increases
Solution Approach 1:
The system divides the intersection into discrete lane segments and analyzes vehicle compositions within each segment. By segmenting the analysis into individual lanes with specific vehicle types (cars, trucks, motorcycles), the system achieves precise lane selection recommendations while managing computational complexity through structured breakdown of the problem space.
Solution Approach 2:
The system changes analytical parameters by detecting vehicle types (car, truck, motorcycle) and their acceleration characteristics, then uses these parameter variations to calculate optimal lane choices. This parameter-based approach enables accurate predictions of lane clearance times without requiring overly complex modeling.
3Loss of time
If the system detects and analyzes vehicle types in each lane, then travel time optimization improves, but information processing requirements increase
Solution Approach 1:
The system extracts only the critical information needed for lane selection: vehicle type (car, truck, motorcycle) and presence in each lane. By filtering out unnecessary sensor data and focusing only on vehicle classification and lane positioning, the system reduces information processing requirements while maintaining the ability to optimize travel time through accurate acceleration profile predictions.
Data Source
AI summary
A vehicle is provided. The vehicle includes a plurality of sensors. The vehicle also includes a vehicle controller. The vehicle controller is programmed to (i) detect a first traffic signal in a direction of travel of the vehicle based on the first plurality of sensor information, where the direction of travel includes a left turn at a traffic signal; (ii) determine a status of the first traffic signal based on the first plurality of sensor information; (iii) determine a first timing for the left turn at the first traffic signal based on the status of the first traffic signal; (iv) determine a second timing for the left turn at a second traffic signal, wherein the second traffic signal is subsequent to the first traffic signal; (v) compare the first timing with the second timing to determine a preferred route; and (vi) present the preferred route.